Systems, methods, and computer products for distinguishing between organizational states and types

Abstract

The present invention discloses a method for distinguishing tissue states or types, comprising: receiving a sequence of thermal data of a tissue, wherein the sequence is sampled at at least one location of the tissue when the tissue is thermally disturbed; deriving at least one tissue-related thermal variable associated with each of the tissue locations from the thermal data; segmenting the tissue into segments, the segments including locations having corresponding at least one thermal variable; and generating an output indicating the tissue segments. The present disclosure also includes a system and a computer product for distinguishing tissue states or types.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 683,836, filed on June 12, 2018, entitled “SYSTEM, METHOD AND COMPUTER PRODUCT FOR DIFFERENTIAL IDENTIFICATION OF TISSUE TYPES IN SITU.”

[0003] This application also relates to international patent application number PCT / IL2015 / 050392 of the same inventor published under PCT publication number WO2015 / 159284, entitled “A DEVICE AND METHOD FOR CANCER DETECTION, DIAGNOSIS AND TREATMENT GUIDANCE USING ACTIVE THERMAL IMAGING”, the contents of which are all incorporated by reference as if fully set forth herein in their entirety. Technical Field

[0004] The present invention, in some embodiments thereof, relates to thermal data collection systems, methods, and computer products. Background Art

[0005] Thermography is a field in which thermal radiation (such as infrared radiation emitted from an object) is detected by a sensor (e.g., a thermography camera) which converts the sensed thermal radiation into an image (a thermogram). A thermogram allows observation of differences in thermal radiation emitted from various areas above the surface of the imaged object.

[0006] In the absence of external thermal intervention (passive thermography), the thermal radiation emitted from an object can be higher or lower than the emitted background thermal radiation. Passive thermography has many applications, such as personnel monitoring against background and medical diagnostics (particularly thermal).

[0007] Unlike passive thermography, an energy source can actively heat the object (active thermography) to create a thermal contrast between the object and the background. Active thermography is used when the object being inspected is in equilibrium with its surroundings.

[0008] The foregoing examples of the related art and limitations related thereto are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those skilled in the art after reading the specification and studying the drawings. Summary of the invention

[0009] The following embodiments and aspects thereof are described and illustrated in conjunction with systems, tools, and methods which are intended to be exemplary and illustrative, not limiting in scope.

[0010] According to one aspect of some embodiments of the present invention, a method for distinguishing tissue states or types is provided, comprising: receiving a sequence of thermal data of a tissue, wherein the sequence is sampled at at least one location of the tissue when the tissue is thermally disturbed; deriving at least one thermal variable associated with each of the tissue locations from the thermal data; segmenting the tissue into segments, the segments comprising locations having corresponding at least one thermal variable; and generating an output indicating the tissue segments.

[0011] According to some embodiments, thermal data is received from at least one of the following: thermal imaging, infrared (IR) sensors, mercury thermometers, resistance thermometers, thermistors, thermocouples, semiconductor-based temperature sensors, pyrometers, gas thermometers, laser thermometers, and ultrasound. In some embodiments, thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels of an image. In some embodiments, at least one thermal variable is selected from the group consisting of: tissue biological metabolic heat source, heat loss due to blood perfusion, blood temperature, tissue density, specific heat, tissue thermal conductivity factor, tissue thermal conductivity coefficient, tissue thermal conductive surface area, tissue surface temperature, and time-dependent thermal gradient between tissue and ambient temperature.

[0012] In some embodiments, the at least one thermal variable further comprises at least one of an ambient temperature and a heat source temperature. In some embodiments, the thermal perturbation comprises at least one of: actively effecting a temperature change in at least a portion of the tissue from an initial temperature to a final temperature, actively effecting a temperature change in at least a portion of the tissue for a specified period of time, passively allowing a temperature change in at least a portion of the tissue from an initial temperature to a final temperature, and passively allowing a temperature change in at least a portion of the tissue for a specified period of time.

[0013] According to some embodiments, the method includes extracting a set of features based on at least some of the thermal data and thermal variables, wherein the features are selected from a feature group including the following items: features representing various derivative values ​​of the variables, features representing noise in the variables, features based on attenuation equations, features based on Fourier series, and correlation features based on differences in features. In some embodiments, the segmentation is further based on a set of features corresponding to the location. In some embodiments, the correspondence is determined at least in part based on the difference values ​​of all variables and features not exceeding a specified threshold. In some embodiments, the method also includes determining the tissue state or type associated with each segment in the segment based at least in part on correlating at least one thermal variable with predetermined values ​​of thermal variables associated with multiple tissue states or types. In some embodiments, the correlation also includes correlating the features.

[0014] In some embodiments, the deriving, segmenting, extracting, and determining are performed by a machine learning classifier that is trained in a training phase on a training set that includes a plurality of thermal data sequences, each thermal data sequence being sampled at at least one location in the tissue when the tissue is thermally disturbed; and a label associated with a state or type of at least one location.

[0015] In some embodiments, the method further includes applying, during an inference phase, a trained machine learning classifier to at least one target thermal data sequence sampled at a location of the tissue when the tissue is thermally disturbed to determine a state or type of the tissue location.

[0016] According to one aspect according to some embodiments of the present invention, there is provided a computer program product comprising a non-transitory computer-readable storage medium having program code embodied therein, the program code being executable by at least one hardware processor to: receive a sequence of thermal data of a tissue, wherein the sequence is sampled at at least one location of the tissue when the tissue is thermally disturbed; derive at least one tissue-related thermal variable associated with each of the tissue locations from the thermal data; segment the tissue into segments, the segments comprising locations having corresponding at least one thermal variable; and generate an output indicating the tissue segments. In some embodiments, the at least one thermal variable indicates a state or type of the tissue.

[0017] In some embodiments, thermal data is received from at least one of: thermal imaging, infrared (IR) sensors, mercury thermometers, resistance thermometers, thermistors, thermocouples, semiconductor-based temperature sensors, pyrometers, gas thermometers, laser thermometers, and ultrasound. In some embodiments, thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels. In some embodiments, thermally agitating tissue comprises actively or passively effecting a temperature change from an initial temperature to a final temperature on at least a portion of the tissue.

[0018] In some embodiments, thermally agitating the tissue comprises achieving a temperature change over at least a portion of the tissue within at least one predetermined time period. In some embodiments, at least one tissue-related thermal variable comprises at least one intrinsic tissue thermal parameter that affects thermal behavior of cells. In some embodiments, the computer program product is configured to calculate a set of features based on at least some of the thermal data and the thermal variables.

[0019] In some embodiments, the features are selected from a group of features including: features representing various derivative values ​​of a variable, features representing noise in a variable, features based on an attenuation equation, features based on a Fourier series, and correlation features based on differences in features. In some embodiments, the segmentation is further based on a location having a corresponding set of features.

[0020] In some embodiments, the correspondence is determined based at least in part on all variables and features having difference values ​​not exceeding a specified threshold. In some embodiments, the deriving includes calculating a set of thermal features for each of the tissue locations based at least in part on at least one thermal variable.

[0021] According to one aspect of some embodiments of the present invention, a system is provided, comprising: a thermal sensor configured to sample a sequence of thermal data from at least one location on a tissue when the tissue is thermally disturbed; and a processor configured to: derive at least one tissue-related thermal variable associated with each of the tissue locations from the thermal data; segment the tissue into segments comprising locations having corresponding at least one thermal variable; and generate an output indicative of the tissue segments, wherein the system comprises a heating source or a cooling source directed at least toward the tissue surface and configured to actively heat or cool the tissue.

[0022] In some embodiments, at least one thermal variable indicates a state or type of tissue. In some embodiments, thermal data is received from at least one of: thermal imaging, infrared (IR) sensors, mercury thermometers, resistance thermometers, thermistors, thermocouples, semiconductor-based temperature sensors, pyrometers, gas thermometers, laser thermometers, and ultrasound. In some embodiments, thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels.

[0023] In some embodiments, thermally perturbing tissue comprises actively or passively effecting a temperature change from an initial temperature to a final temperature on at least a portion of the tissue. In some embodiments, thermally perturbing tissue comprises effecting a temperature change on at least a portion of the tissue over at least one predetermined time period. In some embodiments, at least one tissue-related thermal variable comprises at least one intrinsic tissue thermal parameter that affects the thermal behavior of cells.

[0024] In some embodiments, thermal data and thermal variables. In some embodiments, the features are selected from a group of features including: features representing various derivative values ​​of the variables, features representing noise in the variables, features based on attenuation equations, features based on Fourier series, and correlation features based on differences in features. In some embodiments, the segmentation is further based on a corresponding set of features for the location.

[0025] In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and by study of the following detailed descriptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Exemplary embodiments are shown in the accompanying drawings. Dimensions of components and features shown in the drawings are generally chosen for convenience and clarity of illustration and are not necessarily shown to scale. The drawings are listed below.

[0027] Figure 1 is a simplified diagram of a thermal imaging system for distinguishing and / or identifying tissue states or types according to some embodiments of the present invention;

[0028] Figure 2A and Figure 2B is a graph of thermal curves associated with biothermal behavior of tissue cells under temperature changes according to some embodiments of the present invention;

[0029] Figure 3 is a graph of thermal curves associated with biothermal behavior of tissue cells under temperature changes according to some embodiments of the present invention;

[0030] Figure 4 is a graph of thermal curves associated with biothermal behavior of tissue cells under temperature changes according to some embodiments of the present invention;

[0031] Figure 5A , Figure 5B and Figure 5C is a graph indicating peak temperature points according to some embodiments of the present invention;

[0032] Fig. 6A , Figure 6B , Figure 6C and Fig.6D is an exemplary screen image of a thermal imaging system display according to some embodiments of the present invention;

[0033] Figure 7 is an exemplary simplified flow chart illustrating the operation of a thermal imaging system processor according to some embodiments of the present invention;

[0034] Fig. 8A and Figure 8B is a simplified graph showing a difference data set of cross-sectional points according to some embodiments of the present invention;

[0035] Fig.9A is a simplified illustration of a plan view of heat distribution over a portion of a tissue surface according to some embodiments of the present invention;

[0036] Fig. 9B is a thermal profile of cells within a portion of a tissue surface according to some embodiments of the present invention;

[0037] Fig. 10A , Fig. 10B and Fig. 10Cis a simplified illustration of a plan view of heat distribution over a portion of a tissue surface according to some embodiments of the present invention;

[0038] Fig.11A and Fig. 11B is a simplified illustration of a plan view of heat distribution over a portion of a tissue surface according to some embodiments of the present invention;

[0039] Fig.12 are simplified graphical and plan view illustrations of heat distribution over a portion of a tissue surface according to some embodiments of the present invention;

[0040] Fig.13 is an exemplary simplified flow chart illustrating the operation of a thermal imaging system processor according to some embodiments of the present invention;

[0041] Fig.14A and Fig. 14B is a graph of a thermal curve associated with the biothermal behavior of a heated cell according to some embodiments of the present invention;

[0042] Fig.15 is a graph of a thermal curve associated with the biothermal behavior of a heated cell according to some embodiments of the present invention; and

[0043] Fig.16A and Fig. 16B is a simplified illustration of a cross-sectional view of heat distribution within a portion of a tissue surface according to some embodiments of the present invention. DETAILED DESCRIPTION

[0044] According to an aspect of some embodiments of the invention, there is provided a method for distinguishing one or more tissue locations or regions, the method being based at least in part on thermal properties associated with the locations or regions calculated from thermal data. In some embodiments, thermal data is collected from at least one location on the tissue. In some embodiments, the method comprises collecting a time series of thermal data for the at least one location while simultaneously affecting thermal changes in the tissue.

[0045] As used herein, the term "tissue" includes any body or body-related material, such as soft tissue, hard tissue, and cellular and non-cellular material, for example, muscle, bone, teeth, and / or bacteria.

[0046] In some embodiments, the present disclosure provides for distinguishing the state and / or type of tissue. In some embodiments, tissue types include, but are not limited to, muscle, bone, neural tissue, blood vessels, glandular tissue, and / or adipose tissue.

[0047] In some embodiments, tissue states include, but are not limited to, tissue clinical states, such as normal tissue, inflamed tissue, tissue tumor, tissue dysplasia, mucosal cyst, fibroma, fibroepithelial polyp, pathological tissue, precancerous tissue, and / or cancerous tissue.

[0048] Although tissue thermal data can be sampled by many sensing devices, such as infrared (IR) sensors, mercury thermometers, resistance thermometers, thermistors, thermocouples, semiconductor-based temperature sensors, pyrometers, gas thermometers, laser thermometers, and ultrasound, for purposes of clarity and simplicity, by way of example and not limitation, determining the state of one or more tissue locations based on thermal properties calculated from data received via thermal imaging is demonstrated below.

[0049] According to one aspect of some embodiments of the present invention, a method for distinguishing tissue according to tissue state or type is provided, the method being based at least in part on a temperature change affecting at least one surface of at least a portion of the tissue. For example, in some embodiments, effecting the temperature change comprises heating at least one surface of at least a portion of the tissue from an initial base temperature within a predetermined first time period (e.g., t0 to t1), and then allowing the temperature of the tissue to passively return (e.g., cool) to the base temperature within a second time period (e.g., t1 to t2).

[0050] In some embodiments, during the combined first and second time periods t0 to t2, a sequence of thermal data (e.g., thermal images (e.g., video streams)) of at least the tissue surface is obtained using one or more suitable thermal imaging devices (e.g., infrared (IR), near infrared (NIR), short wave infrared (SWIR), and / or another imaging device). In some embodiments, additional images and / or image streams may be obtained during at least a portion of the time period t1 to t2. In some embodiments, the additional images may include red green blue (RGB) images, monochrome images, ultraviolet (UV) images, multispectral images, and / or hyperspectral images.

[0051] In some embodiments, the image data is processed to extract a plurality of values ​​associated with at least some pixels in each image. In some embodiments, a portion of the values ​​may be extracted at a point in time and / or as a time series over part or all of the time period t0 to t2.

[0052] In some embodiments, the one or more values ​​can be converted into one or more feature vectors, including multiple time-dependent feature vectors. In some embodiments, the one or more feature vectors can be compared to predetermined features or feature vectors associated with one or more tissue states or types. In some embodiments, the state of one or more regions of the tissue can be determined based at least in part on the comparison.

[0053] In some embodiments, the one or more feature vectors for each pixel are grouped into one or more groups indicating that the group is a region of tissue state or type of tissue being imaged.

[0054] In some embodiments, the present disclosure provides an output indicating the tissue state or type of one or more regions of a thermally imaged tissue. In some embodiments, the output may include an image including a graphical representation of one or more regions based at least in part on the identified tissue state or type associated with each region. For example, in some embodiments, the boundaries of each region may be delineated, and / or one or more color schemes may be used to present a portion or all of a region. In some embodiments, the graphical representation may be generated as a thermal image, an RGB image, and / or another and / or different types of images. In some embodiments, the boundaries of the identified tissue state or type are mapped on a tissue state distribution map. In some embodiments, the boundaries of the identified tissue state or type are drawn in the form of a curve graph such as a histogram.

[0055] In some embodiments, a machine learning classifier can be trained on a data set that includes a set of feature vectors associated with multiple subject tissues, where the training data set can be labeled with one or more tissue states or types present in several regions of the subject tissues. In some embodiments, the trained classifier of the present disclosure can then be applied to a target feature set from a target subject to determine the presence of one or more physiological or pathological parameters in the target tissue.

[0056] In some embodiments, actively changing the temperature of the tissue includes actively heating or actively or passively cooling a portion of the tissue during at least a portion of the imaging time period.

[0057] In some embodiments, processing or analysis is performed on each pixel of the acquired image. In some embodiments, the analysis includes extracting a plurality of pixel-level values ​​for each pixel that represent a quantification of a physiological or pathological parameter.

[0058] In some embodiments, the method includes receiving a sequence of thermal data of a tissue, wherein the sequence is sampled at one or more locations of the tissue when the tissue is thermally disturbed; deriving from the thermal data at least one tissue-related thermal variable associated with each of the tissue locations; segmenting the tissue into segments including locations having corresponding one or more thermal variables; and generating an output indicative of the tissue segments.

[0059] In some embodiments, the method includes calculating a set of features based on at least some of the thermal data and thermal variables, in which case the segmentation is based on the location having a corresponding set of features. In some embodiments, the correspondence is determined based at least in part on the difference values ​​of all variables and features not exceeding a specified threshold. In some embodiments, the method includes calculating a set of thermal features for each of the tissue locations based at least in part on one or more thermal variables. In some embodiments, the one or more thermal variables indicate a state or type of tissue. In some embodiments, the features are selected from a feature group including: features representing various derivative values ​​of variables, features representing noise in variables, features based on attenuation equations, features based on Fourier series, and related features based on differences in features.

[0060] In some embodiments, the method includes acquiring a sequence of thermal images within a time period. In some embodiments, for each pixel and / or measurement point, a pixel value is extracted from the thermal image, a feature vector representing the thermal properties of tissue cells within the time period is generated, pixels with similar features are clustered, and the pixel clusters are reflected on corresponding areas in the imaged tissue. In some embodiments, the method includes determining the tissue state or type of at least one area based on comparing the feature with a known feature set of the tissue state or type. In some embodiments, the method includes generating an output, for example, a graphical representation of the tissue state in one or more areas. In some embodiments, the method includes using a trained machine learning classifier to classify the tissue state in each area. In some embodiments, generating a feature vector is optional, and the method includes determining the tissue state or type of at least one area based on comparing the feature with a known feature set of the tissue state or type.

[0061] In some embodiments, the method includes generating a graph representing the distribution of thermal variables and / or thermal features over a portion of tissue within an imaging field of view (FOV). In some embodiments, the method includes analyzing the distribution of pixel-level values ​​on the graph, and identifying clusters of values, each cluster being within a same pixel value range, and associating the identified values ​​with a specific tissue type or state. In some embodiments, clusters of pixels in a thermal image that share the same value are associated with corresponding clusters of specific tissue cell types.

[0062] In some embodiments, the distribution analysis of pixel level values ​​is based on calculation of differences between calculated pixel level values.

[0063] In some embodiments, the method includes generating a plurality of pixel level value distribution maps, each pixel level value distribution map is associated with a specific physiological or pathological parameter. In some embodiments, multiple maps generated from the obtained pixel level values ​​are combined or superimposed to enhance the identification of clusters of tissue cell types.

[0064] According to one aspect of some embodiments of the present invention, a method for distinguishing the state or type of tissue is provided. In some embodiments, the method includes obtaining pixel-level values ​​from a thermal image of at least a portion of the tissue within the field of view (FOV) of a thermal imager. In some embodiments, the method includes actively changing the temperature of a portion of the tissue within a set time period. In some embodiments, the method includes obtaining a thermal image (frame) of the tissue during the temperature change. In some embodiments, the method includes processing consecutive frames of the obtained pixel-level values, and extracting pixel-level values ​​related to changes in one or more variables or features within a set time period, which are derived from pixel-level values ​​representing physiological or pathological parameters associated with the tissue.

[0065] In some embodiments, the method includes generating a graph for each pixel representing changes in pixel level values, thermal variables, or features derived from the pixel level values ​​during changes in tissue temperature. In some embodiments, the method includes processing, for example, by performing a comparative analysis on one or more portions of a curve of the graph and identifying groups of pixels having similar or identical curve patterns associated with a particular tissue state or type. In some embodiments, the identified groups of pixels sharing the same pixel level values, variables, or features are associated with a particular type or state of tissue. In some embodiments, the distribution analysis of the pixel level values, variables, or features is based on calculations of differences between graph curves, the graph curves being based on values ​​obtained from each pixel.

[0066] In some embodiments, the method includes actively heating the tissue and allowing the tissue to be passively cooled. In some embodiments, the processing of the imaging frames obtained during the period of active heating and passive cooling is expressed by a graph curve having a growth portion, a peak value, and a decay portion. In some embodiments, the thermal imaging system includes a processor and a computer program product, which is configured to perform a comparative analysis only on the growth portion of the resulting curve. In some embodiments, the comparative analysis is performed only on the decay portion of the resulting curve. In some embodiments, the computer program product of the processor is configured to perform a comparative analysis on the curve peak temperature only at the intersection of the growth portion and the decay portion of the resulting curve.

[0067] Alternatively and optionally, in some embodiments and for example Figure 2BAs shown, the method includes actively cooling the tissue and allowing the tissue to warm up passively. In some embodiments, the analysis of the imaging frames obtained during the period of active cooling and passive warming is expressed by a curve graph having a decay portion, a valley (minimum point), and a growth portion. In some embodiments, the thermal imaging system includes a processor and a computer program product, which is configured to perform a comparative analysis only on the growth portion of the resulting curve. In some embodiments, the comparative analysis is performed only on the decay portion of the resulting curve. In some embodiments, the computer program product of the processor is configured to perform a comparative analysis on the valley value (lowest temperature) of the curve only at the intersection of the growth portion and the decay portion of the resulting curve.

[0068] In some embodiments, the method includes processing by performing a comparative analysis of the differences in the seasonal noise of the curves from each pixel and identifying clusters of pixels having similar seasonal noise of the curves associated with a tissue state or type. In some embodiments, the identified clusters are associated with tissue cell clusters.

[0069] In some embodiments, the method includes generating a plurality of distribution maps, each distribution map based on generated pixel-level values ​​associated with a particular physiological parameter. In some embodiments, the plurality of generated maps are combined or superimposed to enhance identification of the state or type of tissue cell clusters.

[0070] In some embodiments, the method includes gradually actively heating or cooling a portion of the tissue. In some embodiments, the method includes obtaining thermal images (frames) over a set time period. In some embodiments, the method includes processing consecutive frames of images obtained during each active heating or cooling increment, and extracting pixel-level values ​​regarding changes in one or more physiological or pathological parameters associated with the tissue within the heating increment.

[0071] According to one aspect of some embodiments of the present invention, a method for distinguishing the state or type of tissue is provided. In some embodiments, the method includes actively heating the tissue. In some embodiments, heating the tissue includes applying a linear heating energy beam (e.g., infrared light) to one side of the tissue, thereby heating the tissue strip. In some embodiments, the method includes obtaining a thermal image (frame) of a portion of the tissue within the FOV of a thermal imager within a set time period.

[0072] Alternatively and optionally, in some embodiments, the method for distinguishing the state or type of tissue includes actively cooling the tissue. In some embodiments, cooling the tissue includes applying a linear beam of cooling energy (e.g., a spray or contact coolant) to one side of the tissue, thereby cooling the tissue strip. In some embodiments, the method includes obtaining a thermal image (frame) of a portion of the tissue within the FOV of the thermal imager over a set period of time.

[0073] In some embodiments, the method includes processing consecutive frames of images acquired over a time period and extracting pixel-level values ​​regarding the rate of thermal diffusion in a direction perpendicular to the vector beam and / or heated tissue strip during a set time period. In some embodiments, processing consecutive frames of images acquired over a particular time period includes associating pixel-level values ​​regarding the rate of thermal diffusion in the tissue with one or more physiological or pathological parameters associated with the tissue. In some embodiments, the method includes identifying clusters of pixels that share pixel-level values ​​or variables or features based on pixel-level values ​​associated with diffusion rates within a given range of diffusion rates associated with a tissue type or state. In some embodiments, the identified clusters of pixels are associated with corresponding clusters of cells of a particular tissue type or state on a graphical representation of the imaged tissue.

[0074] According to one aspect according to some embodiments of the present invention, a method for distinguishing the state or type of tissue is provided. In some embodiments, the method includes heating the tissue. In some embodiments, heating the tissue includes applying heating energy (e.g., infrared light) to any portion of the tissue surface. Alternatively and optionally, in some embodiments, the method includes actively cooling the tissue. In some embodiments, cooling the tissue includes applying cooling energy (e.g., spray or contact coolant) to any portion of the tissue surface. In some embodiments, the method includes obtaining a thermal image of a portion of the tissue within the FOV of a thermal imager within a set time period.

[0075] In some embodiments, the method includes processing successive frames of thermal images (frames) acquired over a time period, and extracting pixel-level values ​​regarding the rate of thermal diffusion on a tissue surface during a set time period. In some embodiments, processing successive frames of images acquired over a particular time period includes associating information regarding the rate of thermal diffusion in the tissue with one or more physiological or pathological parameters associated with the tissue. In some embodiments, the method includes identifying clusters of pixels that share a diffusion rate within a given range associated with a tissue type. In some embodiments, the identified clusters are associated with corresponding clusters of cells of a particular tissue type on a graphical representation of the imaged tissue.

[0076] According to one aspect of some embodiments of the present invention, a method for distinguishing the state or type of tissue is provided. In some embodiments, the method includes heating the tissue. In some embodiments, heating the tissue includes applying heating energy (e.g., infrared light) to a predetermined depth within the tissue. In some embodiments, the method includes obtaining thermal images of portions of the tissue at various depths between the tissue surface and the predetermined depth within a set time period.

[0077] Alternatively and optionally, in some embodiments, the method for distinguishing the state or type of tissue includes actively cooling the tissue. In some embodiments, cooling the tissue includes applying cooling energy (e.g., a spray or contact coolant) to a predetermined depth within the tissue. In some embodiments, the method includes obtaining thermal images (frames) of portions of the tissue at various depths between the tissue surface and the predetermined depth over a set period of time.

[0078] In some embodiments, the method includes processing consecutive frames of thermal images obtained at any particular depth within a time period, and extracting pixel-level values ​​regarding the rate of thermal diffusion throughout the tissue layer at the particular depth during the set time period. In some embodiments, processing consecutive frames of images obtained within a particular time period includes associating the pixel-level values ​​associated with the rate of thermal diffusion within the tissue with one or more physiological or pathological parameters associated with the tissue. In some embodiments, the method includes identifying clusters of voxels that share a diffusion rate within a given range associated with a particular tissue type or state. In some embodiments, the identified clusters of pixels are associated with corresponding clusters of cells of the particular tissue type or state on a graphical representation of the imaged tissue.

[0079] According to one aspect of some embodiments of the present invention, there is provided a computer program product comprising a non-transitory computer-readable storage medium having program code embodied therein, the program code executable by at least one hardware processor to process one or more thermal images (frames) of a portion of tissue within a field of view (FOV) of a thermal imager.

[0080] In some embodiments, the computer program product is executable to calculate based on information received from each pixel of the acquired image. In some embodiments, the program product is executable to calculate pixel-level values ​​associated with a physiological or pathological parameter of the tissue. In some embodiments, the program product is executable to generate a map based on the calculated values ​​associated with the physiological or pathological parameter of the tissue. In some embodiments, the program product is executable to indicate clusters of pixels having values ​​associated with parameters within a given parameter range associated with a particular tissue type or state.

[0081] In some embodiments, the computer program product is executable to calculate a series of variables and / or features based on pixel-level values ​​received from at least one pixel and associated with changes in a physiological or pathological parameter of the tissue within a set time period. In some embodiments, the program product is executable to calculate variables and / or features based on multiple image frames captured within a set time period. In some embodiments, the program product is executable to generate a curve based on the calculated variables and / or features, and the calculated variables and / or features are associated with changes in the physiological or pathological parameters of the tissue within a set time period. In some embodiments, the program product is executable to indicate clusters of pixels having similar curves and associated with differences in parameters within a given range or above a given threshold associated with a particular tissue type or state.

[0082] system

[0083] Reference now Figure 1 . This figure is a simplified diagram of a thermal imaging system for distinguishing and / or identifying tissue states or types according to some embodiments of the present invention. In some embodiments, the thermal imaging system 100 for distinguishing and / or identifying tissue states or types includes a thermal imager 108 that images the surface of the tissue 104 when it is actively heated or cooled. In some embodiments, the thermal imager 108 communicates with a processor 110. The processor 110 of the thermal imaging system 100 is configured to process the thermal image obtained by the thermal imager 108 and generate an output image, for example on a display 112.

[0084] In some embodiments, the output graph includes a graphical representation of the calculated pixel level values. In some embodiments, the output graph includes a graphical representation of the calculated pixel level values ​​superimposed on an RGB image or any other graphical representation of the imaged tissue. In some embodiments, the output graph includes a graphical representation of the calculated difference of the calculated pixel level values ​​relative to a gold standard value. In some embodiments, the output graph includes a graphical representation of the calculated difference of the pixel level values ​​superimposed on an RGB image or any other graphical representation of the imaged tissue.

[0085] like Figure 1 As shown, a thermal imaging system 100 for distinguishing and / or identifying tissue states or types includes a heating / cooling source 102 directed toward a surface of a tissue 104 to be treated. The surface of the tissue 104 may be the surface of any tissue or organ in the body, such as skin, liver, spleen, kidney, and bladder. Figure 1 In the illustrated embodiment, the surface of tissue 104 includes clusters of cells of abnormal tissue 106 .

[0086] In some embodiments, active heating may include one or more heating methods selected from a range of heating methods including radiation, convection, and conduction. Heat source 102 may be any suitable heat source, such as a LED. High radiant flux density 400nm violet LED emitter LZP-D0UB00-00U5 manufactured by Inc., San Jose CA 95134, USA or any infrared (IR), radio frequency (RF), ultrasound (US), fluid flow on tissue surface, heated tube or other carrier, etc.

[0087] In some embodiments, active cooling can be applied, for example, by evaporation (e.g., alcohol spray), local coolant spray (nitrogen), flow of cooling fluid over the tissue surface, cooling tubes or other carriers, etc.

[0088] In some embodiments and Figure 1 As shown, the digital thermal imager 108 of the system 100 images thermal radiation 150 emitted from the surface of a section of tissue 104. In some embodiments, the imager 108 is a video thermal imager configured to generate successive frames of thermal images obtained from the surface of the tissue 104 within a field of view (FOV) 155 of the thermal imager 108 over a set period of time. In some embodiments, the thermal imager 108 comprises a digital microscope thermal imager 108. In some embodiments, the thermal imager 108 can be any suitable digital imager, for example, PI 450 thermal infrared camera from Portsmouth, NH 03801 USA. In some embodiments, system 100 includes a visible light camera. In some embodiments, digital imager 108 includes any suitable thermal sensor, such as MRI, ultrasound, thermocouple, or any other sensor that measures temperature.

[0089] In some embodiments, and as explained in more detail elsewhere herein, the system 100 includes an illumination source 114 that illuminates the tissue and delineates a surgical boundary for removing a cell cluster of abnormal tissue. In some embodiments, and as explained in more detail elsewhere herein, the system 100 includes an ablation energy source 116 to ablate the delineated cell cluster of abnormal tissue. In some embodiments, the illumination source 114 and the ablation energy source 116 are generated from a single source (e.g., a laser).

[0090] Processing of data obtained from individual pixels in a thermal image (frame)

[0091] To simplify the description, the examples below refer to an IR digital imager. However, as described elsewhere herein, any other suitable thermal imager or sensor may be used.

[0092] In some embodiments, the digital thermal imager 108 includes one or more pixel arrays. The pixel arrays respond to IR radiation emitted from the imaging surface of the tissue 104. The one or more pixels respond to a corresponding segment (S ) of the imaging surface of the tissue 104 within the pixel's FOV (FOVp). p ) to react to IR radiation emitted by the tissue. In some embodiments, the heat source 102 is configured to gradually and actively heat the surface of the tissue 104 over a set time period, thereafter ceasing active heating and allowing the tissue to passively cool, during which the tissue temperature returns to the temperature before active heating began. Throughout the heating and cooling time period, the thermal imager 108 obtains a series of consecutive frames of thermal images of the surface of the tissue 104. In some embodiments, each consecutive thermal frame in the series of thermal images obtained is time-stamped, and thus a series of two or more frames obtained within a time period provides information about changes in the recorded thermal parameters of the tissue. In some embodiments, the system 100 includes a processor 110 that is configured to process the recorded thermal parameters and map the thermal behavior of the tissue. In some embodiments, and as explained in more detail elsewhere herein, the processor 110 is configured to define the tissue type or state of the imaged tissue based on a thermal behavior map of the imaged tissue.

[0093] In some embodiments, the cooling source 102 is configured to gradually actively cool the surface of the tissue 104 over a set period of time, after which active cooling is stopped and the tissue is allowed to passively warm up, during which the temperature of the tissue returns to the temperature before active cooling began. Throughout the cooling and warming period, the thermal imager 108 obtains a series of consecutive frames of thermal images of the surface of the tissue 104.

[0094] In some embodiments, each consecutive thermal frame in the series of thermal images obtained is time stamped, and thus a series of two or more frames obtained within a time period provides information about changes in the recorded thermal parameters of the tissue. In some embodiments, the system 100 includes a processor 110 configured to process the recorded thermal parameters and map the thermal behavior of the tissue. In some embodiments, and as explained in more detail elsewhere herein, the processor 110 is configured to define a tissue type or state of the imaged tissue based on a map of the thermal behavior of the imaged tissue.

[0095] In some embodiments, the processor 110 of the thermal imaging system 100 includes a non-transitory computer-readable storage medium having a program product embodied therein. The program product is executable by the processor 110 of the thermal imaging system 100 to process (e.g., compare and plot) differences (e.g., differences) between consecutive time-stamped frames expressed by differences between different pixel level values ​​exhibited in at least a portion of tissue within a FOV of a plurality of pixels.

[0096] The processor 110 of the thermal imaging system 100 is configured to process the pixel level value received from each pixel of each consecutive image frame according to the time stamp of each frame, and generate a graph obtained from the pixel level value, which indicates the pixel level value at each position S on the surface of the tissue 104 within a set time period. p The received data represents raster graphic pixel values ​​and / or time-dependent vector pixel values ​​of one or more tissue physiological or pathological parameters at one or more specific points in time.

[0097] In some embodiments, one or more tissue physiological or pathological parameters are at least any of the following: external parameters that affect the thermal behavior of cells, such as ambient temperature, external heat sources and time-dependent thermal gradients inside and between the environment and the object; and / or intrinsic tissue parameters that affect the thermal behavior of cells (thermal parameters), such as tissue and / or organism metabolic heat sources, heat loss due to blood perfusion, blood temperature, tissue density, specific heat, thermal conductivity factor, thermal conductivity coefficient, thermal conductive surface area (m2), temperature of the surface of the object, etc.

[0098] In some embodiments, the processor 110 of the thermal imaging system 100 uses one or more algorithms that use various mathematical expressions to approximate the results obtained as values ​​received from one or more imager pixels and generate variables and / or features based on the pixel-level values ​​that can be plotted to generate an accurate map of the imaged tissue type or state.

[0099] In some embodiments, for example, the calculated pixel-level value-based features are expressed by mathematical expressions. For illustration purposes only, in one example, the pixel-level value (i.e., the temperature at a pixel at a given time) is expressed by the following mathematical expression, which is based on the Pennes biothermal conductivity equation:

[0100] T(t)=a+be ct +dt

[0101] Wherein (dt) can be normalized by time, and variables (a), (b), (c) and (d) are variables derived from the Pennes biothermal conductivity equation, which is a widely accepted temperature distribution equation for biological tissues. Variables (a), (b), (c) and (d) are used herein for the purpose of clarity and simplicity, in an exemplary and non-restrictive manner, and may include any number or combination of variables and may be of any type. For example, variables (a), (b), (c) and (d) may be at least any one of the following: external parameters that affect the thermal behavior of cells, such as ambient temperature, external heat sources and time-dependent thermal gradients between the interior and the environment and the object; and / or intrinsic tissue parameters that affect the thermal behavior of cells (thermal parameters), such as tissue and / or organism metabolic heat sources, heat loss due to blood perfusion, blood temperature, tissue density, specific heat, thermal conductivity factor, thermal conductivity coefficient, thermal conductive surface area (m2) and the temperature of the surface of the object.

[0102] In some embodiments, multiple features can be calculated based at least in part on variables (e.g., variables a, b, c, and d), including but not limited to features representing various derivative values ​​of the variables, features representing noise in the variables, features based on attenuation equations, features based on Fourier series, and related features based on differences in features.

[0103] The following expressions include examples of such derivations based on the Pennes equation, expressed as:

[0104]

[0105] In some embodiments, the following assumptions are made:

[0106] a) Transverse contributions and / or heat generation are negligible (the metabolic time scale is one minute [1]) and the following expression is thus obtained:

[0107]

[0108] b)

[0109]

[0110] where C is the area and (h) is the heat transfer coefficient, Tc is the core temperature, and

[0111] c) We assume that f(t) changes slowly with time.

[0112] Based on the public assumptions:

[0113]

[0114]

[0115]

[0116] in

[0117] B≈T i -A

[0118] Equation (4.65) in Analytical Bioheat Transfer: Solution Development of the Pennes' Model, Sid M. Becker, Chapter 4 conforms to this formula in the limit 4αt=l2;l->0.

[0119] T(t~=0) can also be approximated by a linear function or a high-order polynomial:

[0120]

[0121] T(t) is expressed as an index of a short time period (eg, (t) may be between 0 and 40 seconds, between 10 and 30 seconds, between 15 and 25 seconds, or any number of seconds in between).

[0122] In another example and in some embodiments, variable (a) may express the initial condition at the transition point from active heating / cooling of the imaged tissue to passive cooling or warming and is non-time dependent. In some embodiments, variables (b) and / or (c) express a combination of tissue physiological or pathological parameters, such as density (ρ), specific heat (C), and thermal conductivity (K).

[0123] As explained in more detail elsewhere herein, in some embodiments, the extracted variables (a), (b), (c), (d) and other contributing variables, clusters of the same variables, or clusters of variables from one or more thermal images are processed together with a mathematical expression or combination of mathematical expressions by a computer program product of processor 110 using a data mining process, for example, to cross-reference the data, perform data cleaning and generate output in the form of a graph that indicates and / or identifies various tissue states or types within the imaged tissue region.

[0124] In some embodiments, the following are alternative exemplary expressions for a body without an internal heat source:

[0125] 1.T(t)=a+be -ct

[0126] 2.

[0127] in:

[0128] Ti is the initial temperature of the body.

[0129] T∞ is the ambient temperature.

[0130] parameter

[0131] in is the convective thermal conductivity.

[0132] is the thermal conductivity.

[0133] 3. (Use normal temperature).

[0134] In some embodiments, the following are alternative exemplary expressions for a body having an internal heat source:

[0135] 1.T(t)=a+be -ct -dt

[0136] 2.

[0137] 3.

[0138] 4.

[0139] where (h) is the convection factor (eg, transfer of heat from tissue to air) and therefore depends on the ambient temperature.

[0140] In some embodiments, the computer program product of processor 110 is configured to compare one or more potential output maps based on each acquired image to a gold standard and select at least one of: adjusting the analysis process (e.g., by changing selected variables, selected combinations of mathematical calculations, and other mathematical and / or statistical operations), generating an output map that expresses the difference between the acquired data and the data of the gold standard, or not generating an output map if no difference exists.

[0141] Figure 2A , Figure 2B , Figure 3 , Figure 4 , Figure 5A , Figure 5B , Figure 5C , Fig. 9B , Fig.12 , Fig.14A , Fig. 14B and Fig.15 The curve in the graph represents the temperature (T) measured in image frames per second (FPS) 0c) Changes over time (t). For example, in the case where images are acquired at a rate of 25 FPS, every 25 frames represent one second.

[0142] Reference now Figure 2A and Figure 2B , which are graphs of thermal curves associated with the biothermal behavior of heated tissue according to some embodiments of the present invention. In some embodiments, curve 200 represents the change in temperature (T) of cells within a tissue segment (Sp) from a base temperature (Tb) based on IR radiation emitted from each Sp on the surface of tissue 104 within a set time period (t0 to t1). In some embodiments, curve 200 expresses the biothermal behavior of the tissue in response to heating within a set time period (t0 to t1), and includes a growth portion 202 in response to heating, a decay portion 204 during a cooling time period (t1 to t2), and a peak temperature 206 at the intersection (t1) of the growth portion 202 and the decay portion 204. As explained in more detail elsewhere herein, the set time period (t0 to t1) is a period of time when the temperature of the cells in the tissue segment (Sp) changes from a base temperature (Tb). n ) does not necessarily reflect that the heating period value is followed by a cooling period, and can be broken down into time periods including various temperature change modes.

[0143] As explained in International Patent Application No. PCT / IL2015 / 050392 by the same inventors, various states or types of tissue exhibit specific biothermal behaviors expressed by one or more of a specific growth portion 202, a specific decay portion 204, and a specific curve peak temperature 206. In some embodiments, the thermal imaging system 100 images the tissue over a period of time (t0 to t2) and processes the data received from the pixels of each successive frame of the image according to the timestamp of each frame and generates a growth portion 202 specific to the imaged tissue.

[0144] Similarly, in some embodiments, the thermal imaging system 100 processes the data received from the pixels of each consecutive frame of the image according to the timestamp of each frame and generates an attenuation portion 204 that is specific to the imaged tissue. Thus, the thermal imaging system 100 can combine the specific growth portion 202 and the attenuation portion 204, calculate the intersection point of the curve portions 202 and 204, and generate a value for each pixel that expresses the location of the peak temperature 206 on the generated curve 200.

[0145] As disclosed elsewhere herein, in some embodiments, the method implemented via system 100 includes actively changing the temperature of the tissue during at least a portion of the imaging time period (e.g., t0 to t1). In some embodiments, the acquired frames provide information about changes in physiological or pathological parameters of the tissue during the imaging time period. In some embodiments, actively changing the temperature of the tissue includes actively heating or actively cooling a portion of the tissue during at least a portion of the imaging time period.

[0146] In some embodiments, as described elsewhere herein, data may be extracted from at least some portions of the active heating and active cooling phases to improve the accuracy of the output graphs generated by the computer program product of processor 110 .

[0147] To simplify the description, the following examples only relate to method embodiments that include heating followed by cooling. However, all disclosed method embodiments can be implemented in the same manner, using active cooling instead of active heating, such as cooling followed by heating.

[0148] In some embodiments, the curve 200 generated by the processor 110 of the thermal imaging system 100 is a curve generated based on values ​​obtained from a single thermal image pixel within a set time period (e.g., from a set of consecutive thermal images taken within a set time period). In some embodiments, the generated curve represents a thermal signature of a particular imaged tissue type or state. Figure 3 In the exemplary embodiment shown, the figure is a graph of thermal curves associated with the biothermal response of heated tissue according to some embodiments of the present invention, and a pair of thermal response curves obtained from two pixels P1 and P2 are compared by plotting on the same T / t coordinate system.

[0149] like Figure 3 As shown, the curve 300 obtained from the pixel P1 increases steeply relative to the curve 300' obtained from the pixel P2, and reaches the peak temperature 206 earlier than the peak 206' of the curve 300'. The peak temperature 206 is also at a higher temperature (e.g., 45.05°C) with reference to the peak temperature 206' of the curve 300' (e.g., 44.90°C). The difference between the curves 300 and 300' is also shown in the decay portions 204 and 204', where the decay portion 204 of the curve 300 is steeper relative to the decay portion 204' of the curve 300', for example, reaching a temperature of about 44°C after about 1400 seconds, with reference to the decay portion 204', which reaches the same temperature after about 1750 seconds. For simplicity of description and in order not to be limited by any example, the temperature (T) in the graphs depicted in the accompanying drawings is scaled by continuous natural numbers.

[0150] Similar to the thermal signature obtained from the differences between the thermal behavior curves of various tissue states or types, the shape of the thermal behavior curve that results in the peak temperature 206 / 206' and the decay therefrom also varies between thermal behavior curves and can be identified as being associated with a particular tissue type or state by the processor 110 of the thermal imaging system 100. In some embodiments, the processor 110 of the thermal imaging system 100 processes the values ​​received from at least a portion of the pixel array, as explained in more detail elsewhere herein, and manipulates the values ​​to generate an indication of a tissue state or type in the imaged tissue.

[0151] Thus, in some embodiments, the thermal imaging system 100 may identify the thermal behavior curves 300 and 300' as being specific to one or more tissue states or types, and thus may be used to generate outputs indicative of different tissue states or types in the imaged tissue. In some embodiments, the output from the processor 110 of the thermal imaging system 100 may be compiled into a lookup table that associates thermal signatures derived from the thermal behavior curve graphs with specific tissue types or states that may be individually identified and histologically verified.

[0152] As disclosed elsewhere herein, differences are consistently exhibited along the thermal behavior curve, and thus, variable length (timeline) processing portions of the curve are achieved, such as only the growth portion 202, only the decay portion 204, only the location of the peak temperature 206, or any portion or combination thereof.

[0153] Reference now Figure 4 , which is a graph of thermal curves associated with the biothermal behavior of heated tissue cells according to some embodiments of the present invention. Figure 4 An exemplary embodiment of a pair of thermal behavior curves 400 and 400' obtained from two pixels P3 and P4 over a time period (t) is shown and compared by plotting on the same T / t coordinate system. The difference between the features representing the portions of the curves 400 and 400' can be defined not only along the location of the growth portion 402 / 402', the decay portion 404 / 404' and / or the peak temperature 406 / 406' (as described elsewhere herein), but also between the features representing seasonal noise along the portions.

[0154] like Figure 4 As shown in the exemplary segment A of , there is a difference between the respective curves 404 and 404' in terms of the level of seasonal noise 410 and 410' measured relative to the mean median of the curves and represented by imaginary lines 450 and 450', respectively. Figure 4In the exemplary embodiment shown, the level of noise 410 of curve 400 relative to the average median value of the thermal behavior curve is greater than the level of noise 410' of curve 400'. The authors of the present disclosure have discovered that the differences between features based on the level of noise relative to the average median value of the thermal behavior curve are specific to tissue types or states, and therefore can be used to indicate the presence of different tissue states or types in the imaged tissue.

[0155] Reference now Figure 5A , Figure 5B and Figure 5C (collectively, FIG. 5 ), which is a graph analysis of peak temperature points 206 / 406 for comparison by plotting on the same T / t coordinate system according to some embodiments of the present invention. Peak temperature points 206 and 206' are derived from thermal behavior curves 200 / 200', respectively, and peak temperature points 406 and 406' are derived from thermal behavior curves 400 / 400', respectively, as described elsewhere herein. As shown in the exemplary graph shown in FIG. 5 , the difference between peak temperatures 206 and 206' is expressed in terms of temperature and / or time to reach the peak temperature. However, the difference between peak temperatures 406 and 406' is expressed only in terms of temperature, and peak temperatures 406 and 406' are shown as arriving at the same time. The processor 110 of the thermal imaging system 100 is configured to identify differences in the coordinates of peak temperatures 206 / 206' and 406 / 406', and thereby to indicate the presence of different tissue states or types in the tissue being examined.

[0156] In some embodiments and Figure 5C As shown, the figure is a portion 475 of an exemplary embodiment of a pair of thermal behavior curves 400 and 400' obtained from two pixels P3 and P4 (at Figure 4 ), and the pair of thermal behavior curves 400 and 400' are compared by plotting on the same T / t coordinate system, and the computer program product of the processor 110 is configured to compare not only the differences between the features based on the peak temperatures 206 and 206' expressed in terms of temperature and / or time to reach the peak temperature, but also the differences between the features based on an analysis of the shape of at least a portion before the peak of the graph (i.e., the growth portion) and / or a portion after the peak of the graph (e.g., the decay portion).

[0157] As described elsewhere herein, based on features representing peak shape analysis, the computer program product of processor 110 is configured to identify on the generated output map thermal signatures specific to the tissue type or state imaged within the FOVp of the pixel, for example, by identifying thermal behavior patterns specific to the cell type.

[0158] refer to Figure 3 , Figure 45 , in some embodiments, the processor 110 of the thermal imaging system 100 collects values ​​from a plurality of pixels of the imager 108 and groups the calculated results, for example, based on one or more of the following characteristics: a growth portion 202 in response to heating, a decay portion 204 during cooling, a peak temperature 206 at the intersection of the growth portion 202 and the decay portion 204, and seasonal noise, and the processor 110 defines a cutoff line between groups showing close or similar distributions. In some embodiments, and as Figure 5B As shown, the graph is a graph of peak temperature points 206 / 406 for comparison plotted on the same T / t coordinate system, which are grouped and identified by the processor 110 of the thermal imaging system 100 based on the characteristics of the peak temperature points 206 / 406 as: an early peak group (502-1, 502-2, 502-3 and 502-4) and a late peak group (504-1, 504-2, 504-3 and 504-4), the early peak group, for example, peaks at less than 1000 frames (for example, at an imaging rate of 25 frames per second, 1000 frames are imaged in four seconds) and is identified as containing normal tissue based on a lookup table generated by the processor 110 of the thermal imaging system 100 (as described elsewhere herein), and the late peak group, for example, peaks only at more than 2000 seconds and is identified as containing cancerous tissue based on the lookup table.

[0159] like Figure 3 , Figure 4 As shown in FIG. 5 , in some embodiments, features representing peak temperature points (such as peak temperature points 206 / 206 ' / 506 and 506 ') may also be identified as thermal signatures of a particular tissue state or type, and clusters of similar peak temperature points identified by the processor 110 of the thermal imaging system 100 may indicate clusters of tissue cells that share the same tissue state in the examined tissue (e.g., cancerous tissue), as explained in more detail elsewhere herein.

[0160] In some embodiments and as disclosed elsewhere herein, a graph is generated by the processor 110 of the thermal imaging system 100 and is shown. Figure 3 and Figure 4 in which, inter alia, the Pennes bioheat equation is based, wherein variables (a), (b), (c) and (d) may be at least any of the following variables, including ambient temperature, external heat sources, tissue and / or organism metabolic heat sources, heat loss due to blood perfusion, blood temperature, tissue density, specific heat, thermal conductivity factor, thermal conductivity coefficient, thermally conductive surface area (m2), surface and internal temperature of an object, and a time-dependent thermal gradient between the environment and the object.

[0161] Characterization of tissue state and / or type

[0162] In some embodiments, the pixel array of the imager 108 images the surface of the tissue 104. In some embodiments, the tissue 104 is preheated. The processor 110 of the thermal imaging system 100 receives and processes the values ​​from each pixel to create a map of the apparent temperature differences over the surface of the object. In some embodiments, a different color is assigned to each temperature value. The resulting color matrix is ​​sent to the memory of the processor 110 of the thermal imaging system 100 and sent to the system display as a thermal map (temperature distribution image) of the surface of the tissue 104.

[0163] Reference now Fig. 6A , Figure 6B , Figure 6C and Fig.6D (collectively referred to as Figure 6), where Fig. 6A FIG. 6 is an exemplary thermal image 600 of a portion of mouse skin tissue 104 shown on display 112 of system 100 according to some embodiments of the present invention. Fig. 6A and Figure 6B As shown, thermal image 600 includes thermal map 602. In some embodiments, the region of interest can be examined by moving pixel group FOV indicator 604. For example, in Fig. 6A and Figure 6B , pixel group FOV indicator 604 is represented by a square white outline that represents the boundaries of the pixel group's collective FOV of the region of interest on the surface of tissue 104. In some embodiments, indicator 604 is controlled, for example, by a joystick, computer mouse, or similar control device. Fig. 6A and Figure 6B In the exemplary embodiment shown, pixel group FOV indicator 604 is placed over a section of the surface of tissue 104 and shows two foci of abnormal tissue 606, such as suspected of being cancerous.

[0164] Fig. 6A An output diagram generated by a computer program product of the processor 110 is shown, which is based on the extracted feature sets (Fa), (Fb), (Fc), (Fd), i.e. clusters of the same feature set or from Fig. 6A Clustering of features of the same thermal image shown, using the Fig. 6A One or more other combinations of mathematical expressions that generate the output graph shown. Fig. 6A In FIG. 6 , the computer program product of processor 110 identifies two abnormal tissue foci 606 that are suspected of being cancerous, for example. In contrast, Figure 6B yes Fig. 6A In the image of 6B, the same FOV is imaged in white light. Fig. 6A The generated output image shown shows that the identified regions 606 behave the same as the tissue surrounding them. Figure 6CAs shown, the gum tissue 608 appears as substantially uniform, evenly colored gum tissue in the RGB photo. Fig.6D The thermal diffusion map of FIG. 6 delineates a large section of abnormal gum tissue 610 relative to surrounding normal tissue 612. In this particular example, the abnormal gum tissue is identified as cancerous tissue.

[0165] In some embodiments, and as Figure 6C and Fig.6D As shown, this figure is an RGB photo of human gums ( Figure 6C ) and processed and generated thermal diffusion maps superimposed on RGB photographs of tissues ( Fig.6D ).

[0166] In some embodiments, a method for distinguishing tissue states or types includes actively changing the temperature of a surface of at least a portion of the tissue from a base temperature (Tb) within a predetermined first time period (t0 to t1), then stopping the temperature change, and allowing the temperature of the tissue to passively return to the base temperature within a second time period (t1 to t2), while obtaining multiple thermal images of the imaged surface of the tissue during the first time period and the second time period (t0 to t2).

[0167] In some embodiments, the method includes: processing thermal values ​​received from pixels of the imager 108 to generate one or more values ​​associated with one or more physiological or pathological parameters of the tissue; comparing the values ​​to a database (e.g., a lookup table of signature data associated with one or more tissue states or types); and generating an output that indicates on the acquired image the identification of the tissue state or type of tissue cells and / or the delineation of boundaries of identified regions of tissue cells in the same tissue state.

[0168] In some embodiments, indicating and / or identifying a tissue state or type comprises one or more of: tracking changes over time in thermal values ​​received from imager pixels, identifying patterns of said changes, and classifying or grouping the patterns of changes into categories or groups, then comparing the classified patterns to signature patterns of tissue states or types, associating each category with a database of predetermined signature patterns of tissue states or types, and identifying the tissue state or type and / or associating a region within the acquired thermal image containing tissue in the identified tissue state or type.

[0169] The following is an example of the above method used to distinguish between organizational states or types. Figure 7 , which is an exemplary simplified flow chart illustrating the operation of the processor 110 of the thermal imaging system 100 according to some embodiments of the present invention. Figure 7As shown, at 702 , the processor 110 of the thermal imaging system 100 is configured to acquire a sequence of thermal images of a surface of the tissue 104 from the imager 108 over a period of time.

[0170] In some embodiments, and as explained elsewhere herein, the processor 110 of the thermal imaging system 100 is configured to extract, at 703, one or more variables (a), (b), (c), and (d) derived from the Pennes biothermal conductivity equation and / or derive one or more variables (a), (b), (c), and (d) from at least any of: external parameters that affect the thermal behavior of cells, such as ambient temperature, external heat sources, and time-dependent thermal gradients inside and between the environment and the object; and / or intrinsic tissue parameters that affect the thermal behavior of cells (thermal parameters), such as tissue and / or organism metabolic heat sources, heat loss due to blood perfusion, blood temperature, tissue density, specific heat, thermal conductivity factor, thermal conductivity coefficient, thermally conductive surface area (m2), and temperature of the surface of the object.

[0171] In some embodiments, the processor 110 of the thermal imaging system 100 is configured to segment the tissue into segments including locations having corresponding one or more thermal variables at 704 , and optionally generate an output indicative of the tissue segments.

[0172] Optionally, at 705, the processor 110 of the thermal imaging system 100 is configured to calculate one or more features, e.g., (Fa), (Fb), (Fc), and (Fd), for each pixel based on one or more extracted variables (a), (b), (c), and / or (d). In some embodiments, and as explained elsewhere herein, the calculated features (Fa), (Fb), (Fc), and (Fd) represent thermal behavior of imaged tissue cells at least partially affected by thermal parameters, as listed elsewhere herein. In some embodiments and as shown in FIG. 6, the processor 110 of the thermal imaging system 100 displays the calculated features of at least features (Fa), (Fb), (Fc), and (Fd) on the display 600, e.g., in the form of a list 608 or a graph depicting the distribution of the calculated features (Fa), (Fb), (Fc), and (Fd) in the imaging area.

[0173] At 706, the processor 110 of the thermal imaging system 100 compiles one or more feature sets calculated at step 705 from each pixel within the FOV of the imager 108, and processes each set of one or more features (Fa), (Fb), (Fc), and (Fd), and generates a difference map (VFa, VFb, VFc, and VFd) for each set or group compiled at 706 at 708.

[0174] As described elsewhere herein, the calculated features (Fa), (Fb), (Fc), (Fd) and other contributing features, groups of the same features, or groups of features from one or more thermal images are processed by a computer program product of processor 110 using a data mining process, for example, to cross-reference the data, perform data cleaning and generate output in the form of a graph that indicates and / or identifies various tissue states or types within the imaged tissue region.

[0175] In the exemplary embodiment shown in FIG. 6 , the processor 110 of the thermal imaging system 100 displays an output graph generated by a computer program product of the processor 110 on a display 600 that shows the calculated differences in the values ​​of at least the features (Fa), (Fb), (Fc), and (Fd) as a list 610 .

[0176] In some embodiments and optionally and as Figure 7 As shown, at 710, difference maps (VFa, VFb, VFc, and VFd) of each compiled set at 706 of at least features (Fa), (Fb), (Fc) over the FOV of the imager 108 are displayed on, for example, the display 600 in sequence (at 710) or in any combination (e.g., one or more superimposed on each other) (at 712) or in any combination and superimposed on an RGB image of the FOV of the imager 108 (at 714) to identify abnormal tissue (e.g., cancerous tissue) based on a lookup table based on a predetermined gold standard benchmark, which increases the accuracy of the thermal image analysis process.

[0177] In some embodiments and optionally, the processor 110 of the thermal imaging system 100 calculates section points for one or more data sets at 716, e.g., differences between the feature sets generated at 708 (VFa, VFb, VFc, and VFd), and identifies one or more groups of pixels having close or similar calculated section points at 718. At 720, the processor 110 of the thermal imaging system 100 generates a map corresponding to the locations of the identified groups of pixels, according to which analysis of the obtained values ​​results in section points that are closest to the values, variables, and / or features defined by a predetermined gold standard benchmark, and at 722 and 724, the processor 110 of the thermal imaging system 100 overlays the map generated at 720 on the RGB image of the FOV of the imager 108 to assist the health professional in identifying the location of suspicious cell clusters on the surface of the tissue 104.

[0178] In some embodiments, cross-sectional points of one or more data sets (e.g., difference data sets (VFa, VFb, VFc, and VFd)) identified by the processor 110 of the thermal imaging system 100 correspond to consistent regions in an overlap map of the difference data sets (VFa, VFb, VFc, and VFd). In some embodiments, and as explained elsewhere herein, the system 100 includes an illumination source 114 that illuminates the tissue and delineates a surgical boundary for removing abnormal tissue. Alternatively or additionally and optionally, in some embodiments, the system 100 includes an ablation energy source 116 to ablate the delineated abnormal tissue. In some embodiments and optionally, at 724, the processor 110 of the thermal imaging system 100 identifies the location of a suspicious cell cluster on the surface of the tissue 104, and provides the contour coordinates of the suspicious cell cluster to the illumination source 114 that delineates a surgical boundary to remove the abnormal tissue and / or to the ablation energy source 116 that applies ablation energy 116 to ablate the delineated abnormal tissue.

[0179] 6 , the screen image 600 of the display 112 of the system 100 shows a pixel group FOV at a specific set of coordinates (position: 141, 270), which is shown within a square white outline 604. In some embodiments, the frame 604 is located at (position: 141, 270) to identify a suspected abnormal tissue mass 608 as, for example, cancerous or non-cancerous.

[0180] In some embodiments, the method includes performing a rapid shallow thermal scan over a large area and identifying suspicious lesions, and then carefully and meticulously imaging the suspicious lesions or cell areas by placing at least a portion of the area within a square white outline 604 .

[0181] In some embodiments, screen image pixel clusters 606 / 608 represent clusters of pixels (and therefore clusters of imaged tissue cells) that share one or more characteristics, including differences relative to surrounding pixels (and therefore imaged tissue) calculated by the processor 110 of the thermal imaging system 100 based on one or more variables (a), (b), (c), and (d), as described elsewhere herein.

[0182] In some embodiments, and as explained elsewhere herein, a difference map generated for each isolation value of predetermined features (Fa), (Fb), (Fc), and (Fd) within the imager FOV, or any combination thereof, is superimposed on an RGB image of the imaged tissue to make suspicious cell clusters identifiable to the naked eye.

[0183] In some embodiments, the present disclosure may provide implementations of machine learning algorithms and / or techniques, for example, for determining tissue states. In some embodiments, during a training phase, an exemplary machine learning classifier of the present disclosure may be configured to receive, obtain, and / or otherwise have received or obtained a data set including multiple tissue thermal parameters, features, and / or variables associated with multiple subjects. In some embodiments, these thermal parameters, features, and / or variables are the same or substantially similar to those described in greater detail elsewhere herein.

[0184] In some embodiments, the preprocessing stage may include data preparation. Data preparation may include cleaning data, transforming data, and / or selecting a subset of records. In some embodiments, data preparation may include performing preprocessing operations on the data. For example, an interpolation algorithm may be performed to generate values ​​for missing data. Upsampling and / or predictor rank transformations (e.g., for variable selection) may be performed to accommodate class imbalance and non-normality in the data. In some embodiments, performing an interpolation algorithm includes interpolating or estimating values ​​for missing data, such as by generating a distribution of available data for a clinical parameter with missing data, and interpolating values ​​for the missing data based on the distribution.

[0185] In some embodiments, the temporal processing step can be configured to generate a time-dependent representation of one or more parameters, features, and / or variables using, for example, Fourier transforms, polynomial adjustments, decay equations, and / or various statistical tools. In some embodiments, the temporal processing step can include automatically and / or manually combining multiple measurements obtained from a subject over a sequence of time periods to determine and / or create at least one combined parameter and / or feature that can represent a pattern of change of the multiple measurements over time and / or a time series variable.

[0186] In some embodiments, the feature extraction step may be configured to generate additional features, for example based on relationships between existing features in the data set, and add the additional features to the data set.

[0187] In some embodiments, variable selection can be performed to, for example, identify the most relevant variables and predictors from the set of parameters obtained. In some embodiments, variables and / or variable selection can include executing supervised machine learning algorithms, such as constraint-based algorithms, constraint-based structural learning algorithms, and / or constraint-based local discovery learning algorithms. In some embodiments, variable selection can be performed to identify a subset of variables in the training data, which has a desired predictive ability relative to the rest of the variables in the training data, thereby using a model generated based on the selected variables to achieve more effective and accurate predictions. In some embodiments, machine learning algorithms are used to perform variable selection, for example, analysis of differences (ANOVA), boosting integration (such as XGBoost), growth shrinkage ("gs"), incremental association Markov blanket ("iamb"), fast incremental association ("fast, iamb"), maximum minimum parent ("mmpc"), or semi-interleaved Hiton-PC ("si.hiton.pc") algorithms. However, various other implementations of such machine learning algorithms can be used to perform variable selection and other processes described herein. In some embodiments, variable selection may search for variable sets of smaller size that attempt to represent the underlying distribution of the entire variable set, which attempts to increase generalization to other data sets from the same distribution.

[0188] In some embodiments, variable selection can be performed by removing highly correlated variables. Several algorithms can be used to search an input data set with ranked predictors to find a simplified set of variables that best represents the underlying distribution of all variables for infectious complication outcomes. Variable selection filtering algorithms can be used to select a simplified set of variables. For example, in some embodiments, one or more of the maximum minimum parent (mmpc) and / or inter-iamb algorithms can be used to select the nodes of the corresponding Bayesian network as a simplified set of variables.

[0189] In some embodiments, variable selection is performed to search for a subset of variables in the training data for use as nodes of a Bayesian network. A Bayesian network (e.g., a belief network, a Bayesian belief network) is a probability model that uses a directed acyclic graph to represent a set of variables and their conditional dependencies. For example, in the context of diagnosis prediction, variable selection can be used to select variables from the training data for use as nodes of a Bayesian network; if the value of a node for a particular subject is given, a prediction of the subject's diagnosis can be generated.

[0190] In some embodiments, a training data set for a machine learning classification model of the present disclosure is created based at least in part on the collected parameters and the variable selection process performed as described above. In some embodiments, the training data set includes parameters, features, and / or variable sets associated with various tissue states or types of subjects. The values ​​of the parameters can be received and stored for each subject in a plurality of subjects. The training data set can associate the values ​​of multiple parameters, features, and / or variables with the corresponding tissue states of each subject in a plurality of subjects. In some embodiments, the parameters, features, and / or variable sets can be labeled with corresponding tissue states.

[0191] In some embodiments, the machine learning classifier of the present disclosure is trained for a training data set to generate a classification model. For example, the machine learning classifier can perform a classification algorithm (e.g., a binary classification algorithm) to generate a prediction of an organizational state for each subset of model parameters. In some embodiments, the classification algorithm includes, but is not limited to, linear discriminant analysis (IDA), classification and regression tree (CART), nearest neighbor (KNN), support vector machine (SVM), Gaussian support vector machine (GSVM), logistic regression (GLM), random forest (RF), generalized linear model (GLMNET) and / or naive Bayes (NB). In some embodiments, classification can be defined as a task that summarizes the known structure to be applied to new data. The classification algorithm can include linear discriminant analysis, classification and regression tree / decision tree learning / random forest modeling, nearest neighbor, support vector machine, logistic regression, generalized linear model, naive Bayes classification and neural network, etc. In some embodiments, the trained machine learning classification model of the present disclosure can include, for example, cluster analysis, regression (e.g., linear and nonlinear), classification, decision analysis and / or time series analysis, etc. In some embodiments, where variable selection is performed prior to generating a random forest model, the training data is sampled based on a reduced set of variables from the variable selection (as opposed to sampling based on all variables).

[0192] In some embodiments, after the training phase, the trained machine learning classifier of the present disclosure may be configured to implement a validation process, for example, by a first evaluation, which may include, for example, cross validation. Cross validation may be configured to randomly divide the training set into, for example, ten portions. Then, the ten portions of validation may be run ten times, for example, using nine different portions of the training set for machine learning modeling, and using the tenth portion for validation. The results may be evaluated by calculating statistical measurements, for example, the mean and confidence interval of the area under the receiver operating attribute curve (AUROC) for ten different evaluation portions. In some embodiments, the second evaluation may include an evaluation of the machine learning model with respect to the validation set (for example, the tenth portion for validation may include 10% of the original data). In some embodiments, the third evaluation may include statistical analysis, for example, including presenting population characteristics by the median and interquartile range (IQR) of skewed data, and by the mean value of the standard deviation for normally distributed data (for example, using a bootstrap technique). In some embodiments, the cross-validation process of the machine learning model can implement a statistical method configured to estimate the skill of the machine learning model on a finite data sample, for example, to estimate how the machine learning model is expected to perform when used to make predictions on data that was not used when training the machine learning model. In some embodiments, the cross-validation process of the machine learning model can include dividing a given data sample into multiple groups and / or portions, for example, ten groups and / or portions.

[0193] In some embodiments, a trained machine learning classifier of the present disclosure may be applied to a received thermal video stream of tissue during an inference phase to generate one or more predictions regarding the state of regions within the tissue.

[0194] In some embodiments, an unsupervised classification model can be employed, for example, to extract parameters, features, and / or variables in an unsupervised manner from a thermal image stream of tissue. In some embodiments, such extracted parameters, features, and / or variables can then be used as input to the trained machine learning classifier described above.

[0195] Reference now Fig. 8A and Figure 8B , which is a simplified graph showing a difference data set or group of cross-sectional points according to some embodiments of the present invention. In some embodiments, the processor 110 is configured to generate a pixel level value (such as a pixel level value, a thermal variable, or a feature) based on the pixel level value that is closest to the pixel level value, the thermal variable, or the feature defined by the predetermined gold standard benchmark. Fig. 8A and 8B8 ) to select and process differences in a set of pixel-level values ​​or thermal variables or features. As shown in the exemplary graph shown in FIG. 8 , section points 802 and 804 are clustered into one or more clusters (e.g., 802-1, 802-2, 802-3, and 802-4 and / or 804-1, 804-2, 804-3, and 804-4).

[0196] As explained elsewhere herein, the processor 110 of the thermal imaging system 100 is configured to identify associated pixels having generated section points 802 and 804 and delineate a boundary 850 between a first type of tissue associated with the section point 802 in a segment 855 on the surface of the tissue 104 and a second type of tissue associated with the section point 804 in a segment 860 on the surface of the tissue 104. The status or type of the histologically identified tissues 802 and 804 are registered in a lookup table stored in a memory of the processor 110 of the thermal imaging system 100 for future reference.

[0197] Alternatively and optionally, in some embodiments, the processor 110 of the thermal imaging system 100 is configured to compare the clusters 802 / 804 to a pre-compiled lookup table, identify the tissue state or type 802 as a first type of tissue (e.g., healthy tissue) in the tissue segment 855, and identify the tissue state or type 804 as a second type of tissue (e.g., cancerous tissue) in the tissue segment 860, and delineate the boundary 850 between the tissue segments 855 and 860. Additionally and optionally, in some embodiments, the processor 110 of the thermal imaging system 100 is configured to plot the identified tissue states or types 802 and 804 and display the plot on the display 112, which is superimposed on the RGB image of the FOV of the imager 108, such as Figure 8B shown.

[0198] Heat application technology

[0199] Vector Heating

[0200] As used herein, the term "vector" heating refers to heating along a path that may follow any pattern and not necessarily along a straight line.

[0201] Reference now Fig.9A , which is a simplified illustration of a plan view of heat distribution over a portion of a surface according to some embodiments of the present invention; and Fig. 9B , which is a thermal profile of tissue within a portion of a tissue surface according to some embodiments of the present invention.

[0202] like Fig.9AIn the illustrated exemplary embodiment, the tissue surface is heated along a line 902 disposed on one side of the suspected abnormal tissue 904. For clarity of explanation, the heat distribution from the line 902 in a direction away from the suspected abnormal tissue is ignored.

[0203] In some embodiments, the thermal imaging system 100 is configured to obtain multiple thermal images of the FOV of the imager 108 within a set time period (t), and process consecutive frames of the multiple images to extract information about differences in thermal parameters of tissue cells within the set time period.

[0204] In some embodiments, the thermal imaging system 100 compares the heat diffusion rate of tissue cells within the FOV of the imager 108 in one or more time periods (e.g., t1, t2, t3, t4) measured from the heat application time (t0) along the direction indicated by arrow 906. In some embodiments, temperature measurements at time periods (t1), (t2), (t3), and (t4) are taken along lines (e.g., L1, L2, L3, and L4) parallel to the heating line 902.

[0205] like Fig.9A As shown in the illustrated embodiment, during time period (t3), the heat distribution generated by line 902 is uniform over most of the surface of tissue 104 within the FOV of imager 108. However, measurements within region 970 delineated by the dashed circle show that heat diffusion through tissue cells within that region is slower than that of most areas of the surface of tissue 104 (including delineated areas 950 and 960 on either side of region 970). The processor 110 of thermal imaging system 100 is configured to identify the difference in the speed of diffusion through tissue cells within region 970 as being associated with a difference in one or more physiological or pathological / thermal parameters associated with tissue within region 907 and surrounding tissue, and mark region 970 as containing tissue suspected of being abnormal (e.g., cancerous).

[0206] Additionally and optionally, in some embodiments and as described in greater detail elsewhere herein, the processor 110 of the thermal imaging system 100 is configured to process a thermal profile of tissue within the FOV of the imager 108. Fig. 9B As shown in the exemplary embodiment shown in , which is a graph of thermal curves associated with the biothermal behavior of heated tissue according to some embodiments of the present invention, curves 955 / 965 represent thermal curves of tissue surrounding suspected tissue within region 970 (e.g., tissue in regions 950 and / or 960), wherein curve 975 represents a thermal curve obtained from tissue within region 970.

[0207] The graph displayed by the processor 110 of the thermal imaging system 100 shows that the overall thermal behavior (i.e., response to heating) of tissue cells within the region 970 is slower than the thermal behavior (i.e., response to heating) of tissue cells within the region surrounding the region 970 (e.g., region 950 / 960). For example, this is indicated by the shallow growth portion 972 of the curve 975 in response to heating relative to the steeper growth portion 952 / 962 of the curve 955 / 965. Additionally and optionally, the curve 975 reaches the peak temperature 976 later than the curve 955 / 965, which indicates the slower thermal behavior of the tissue within the region 970. Similar to the shallow growth portion 972, the decay portion 974 exhibits the slower thermal behavior of the tissue within the region 970 indicated by the shallow curve relative to the decay portion 954 / 964 of the curve 955 / 965.

[0208] In some embodiments and as explained in more detail elsewhere herein, the processor 110 of the thermal imaging system 100 is configured to process differences exhibited along the thermal behavior curves 975 and 955 / 965 by processing and comparing the curves as a whole or processing only portions of the curves (such as only the growth portions 972 and 952 / 962, only the decay portions 974 and 954 / 964, only the locations of the peak temperatures 976 and 956 / 966, or any combination thereof), and generate a thermal signature derived from the differences between the thermal behavior curves 975 and 955 / 965 (the difference being represented by the shape of the thermal behavior curves before the peak temperature and decaying therefrom), and identify a particular tissue state or type associated with the thermal signature. In some embodiments, the processor 110 of the thermal imaging system 100 processes information received from at least a portion of the pixel array, as explained in more detail herein, and uses the information to indicate the presence of different tissue states or types (e.g., normal tissue versus cancerous tissue) in the examined tissue.

[0209] In some embodiments, the accuracy and specificity of tissue type or condition identification can be increased by heating the surface of tissue 104 along one or more lines 902 disposed to one side of suspected abnormal tissue 904. Fig. 10A , Fig. 10B and Fig. 10C In the exemplary embodiment depicted in FIG. 1 , which is a simplified plan view illustration of heat distribution on a portion of a tissue surface according to some embodiments of the present invention, the surface of tissue 104 is heated along line 902 / 1002 disposed to one side of suspected abnormal tissue 904. In some embodiments, line 902 ( Fig. 10A ) and line 1002( Fig. 10B ) are perpendicular to each other.

[0210] like Fig. 10C As shown, the processor 110 of the thermal imaging system 100 is configured to compile the corresponding Fig. 10Aand Fig. 10B The thermal behavior data obtained from the thermal images 1004 and 1006 shown in the figure are used to extract information about one or more physiological or pathological thermal parameters associated with cells identified as abnormal tissue cells in the thermal images 1004 and 1006 primarily within the area 1008, and at least generate a contour of the suspected abnormal tissue 904.

[0211] In some embodiments, the duality of at least a portion of the values ​​obtained by the processor 110 of the thermal imaging system 100 and the comparison between the values ​​obtained from the image 1004 and the values ​​obtained from the image 1006 increases the accuracy and specificity of the identification and location of the tissue type or state. This enables the processor 110 of the thermal imaging system 100 to zoom in (i.e., enlarge the display) Fig. 10C The area 1008 shown and more accurately depicts the outline 1012 of the suspected abnormal tissue 904. In some embodiments, the processor 110 of the thermal imaging system 100 is configured to superimpose the outline of the suspected abnormal tissue 904 onto the RGB image of the surface of the tissue 104 to assist health professionals in clearly and accurately identifying the boundaries of the suspicious cell clusters 904 on the surface of the tissue 104.

[0212] An example for using vector heating is in tissue boundary analysis, such as Fig.11A and Fig. 11B As shown, this figure is a simplified plan view illustration of heat distribution over a portion of a tissue surface according to some embodiments of the present invention. Fig.11A In the exemplary embodiment shown, the surface of tissue 104 includes two boundary regions 1102 and 1104 that are separated by a boundary line 1106 and appear to include different tissue states, such as one or more spots 1108 or tissue types.

[0213] like Fig.11A As shown in the exemplary embodiment depicted in , the surface of tissue 104 is heated toward one side of a suspicious spot 1112 along a line 1110 disposed substantially on a boundary 1106 between regions 1102 and 1104. For clarity of explanation, heat distribution in a direction away from the suspicious spot from line 902 is ignored.

[0214] To increase the resolution and accuracy of abnormal cell identification, the FOV of the imager 108 is limited to a region 1114 of the surface of the tissue 104. The processor 110 of the thermal imaging system 100 is configured to obtain thermal values ​​from the pixel array imaging the FOV of the imager 108 and process the obtained values ​​as explained elsewhere herein. Fig. 11BAs shown in the exemplary embodiment depicted in , the processor 110 of the thermal imaging system 100 generates a map 1150 that identifies the tissue segment 1112 as abnormal tissue (e.g., cancerous tissue) by marking the tissue segment 1112 by identifying the color or contour within the surface of the tissue 104 of the boundary regions 1102 and 1104 as normal.

[0215] Random spot heating

[0216] Reference now Fig.12 , which is a simplified graphical and plan view of heat distribution over a portion of a tissue surface according to some embodiments of the present invention. Fig.12 As shown, the heat source 102 heats a randomly sized portion 1202 of the surface of the tissue 104. In some embodiments, the randomly sized portion 1202 is heated continuously and uniformly, for example by applying the same level of heat (e.g., Joules) during equal time periods, and continuous thermal images are captured by the imager 108 of the thermal imaging system 100 at given time intervals.

[0217] In some embodiments, and as explained elsewhere herein, the processor 110 of the thermal imaging system 100 processes the acquired images to identify and delineate the tissue regions 1212. For example, in some embodiments, the processor 110 is configured to process and identify temporal temperature uniformity (t u ), at which point a majority (Mc%) of the surface of tissue 104 is imaged as being at the same temperature. In some embodiments, the majority (Mc%) of the surface of tissue 104 is defined by a percentage of the area of ​​the surface of tissue 104 within the FOV of imager 108, such as (Mc%) greater than 50%, between 50%-99%, 60%-90%, and 70%-80%. In some embodiments, processor 110 determines at the endpoint (t u ) to generate a heat map 1250 to identify abnormal tissue segments 1212.

[0218] Fig.12 The exemplary graph depicted in FIG. 1 shows the (t u ) at a temperature level 1204. Fig.12As shown, the graph 1204 exhibits a generally uniform temperature of the tissue along the line QQ, except for a length between L1 and L2 where the temperature is lower. In some embodiments, the lower temperature reached by the tissue along the portion L1-L2 of the line QQ can indicate that the tissue includes a slower growth portion of the thermal curve, as explained in detail elsewhere herein, thereby identifying the tissue as abnormal cells. In some embodiments and as described elsewhere herein, the processor 110 of the thermal imager 100 processes successive thermal images of the abnormal tissue in the L1-L2 portion of the line QQ and processes the growth portion of the thermal curve and identifies the type of abnormal tissue (e.g., cancer cells).

[0219] like Fig.13 As shown in FIG. 1 , this figure is an exemplary simplified flowchart showing the operation of the processor 110 of the thermal imaging system 100 according to some embodiments of the present invention. The processor 110 is configured to obtain at 1302 the time from (t0) to (t u ) from the imager 108, and identifies at 1304 the thermal image at (t u ) has a lower temperature than the majority (Mc%) of the surface of the tissue 104 (e.g., tissue segment 1212). At 1306, the processor generates a thermal map of the surface of the tissue 104 within the FOV of the imager 108 that identifies or delineates clusters of abnormal tissue segments 1212. In some embodiments and optionally, at 1308, the processor 110 overlays the map generated at 1306 on the RGB image of the surface of the tissue 104, and delineates or ablates the abnormal tissue 112 at 1310.

[0220] In some embodiments and at 1312, the processor 110 is configured to process the growth portion of the thermal curve of the cell cluster identified at 1304 and identify at 1314 u ) The tissue type or state (e.g., cancerous) of the tissue segment 1212 having a lower temperature than the majority (Mc%) of the surface of the tissue 104.

[0221] Pulsed heat application

[0222] In some embodiments and Fig.14A and Fig. 14B, which is a graph of a thermal curve associated with the biothermal behavior of heated tissue according to some embodiments of the present invention, the surface of tissue 104 is heated by multiple heating pulses over a time period. In some embodiments, the heat pulses are applied continuously and uniformly, for example by applying the same level of heat (e.g., joules) at equal intervals between the heating pulses during equal time periods. The processor 110 of the thermal imaging system 100 is configured to obtain multiple consecutive thermal images from the imager 108 and process the thermal behavior of the tissue at the surface of the tissue 104 in response to the heating pulses.

[0223] In some embodiments, Fig.14A and Fig. 14B As shown in FIG. 1 and explained in more detail elsewhere herein, in response to applied pulsed heat, different states or types of tissue exhibit different thermal behaviors, as expressed by differences in the thermal curves associated with the thermal behaviors. Fig.14A As shown, thermal parameters obtained from imaged tissue exposed to pulsed heat over a period of time and processed by the thermal imaging system processor 110 exhibit a curve 1402 that includes one or more growth portions 1404, each growth portion is followed by one or more decay portions 1406 and multiple temperature peak points 1408.

[0224] In some embodiments, the processor 110 is configured to perform a top analysis on the curve 1402 and, based on the analysis, identify a thermal signature of the tissue type or state imaged within the pixel-specific FOVp, for example, by identifying temperature peaks (e.g., P1, P2, P3, and P4) of consecutive curves in response to consecutive heating pulses at given times (e.g., t1, t2, t3, and t4), and processing relationships between peaks, such as time intervals between peaks (e.g., i1, i2, i3, and i4), or a calculated growth linear regression 1450 of the peaks.

[0225] In some embodiments, the processor 110 of the thermal imaging system 100 is configured to perform a comparative analysis only on selected portions of the thermal curve (e.g., the growth portion, the decay portion, and / or the peak temperature at the intersection of the growth portion and the decay portion), e.g. Fig. 14B , which exhibits increasing delay periods of curves 1412, 1414, and 1416, i.e., d1 between t1 and t1', d2 between t2 and t2', and d3 between d3 and t3', in response to successive heating pulses. In some embodiments, the processor 110 is configured to identify a thermal signature specific to a tissue type or state imaged within the FOVp of the pixel based on the analysis, for example by identifying a thermal behavior pattern specific to the tissue type or state.

[0226] Sectional heating

[0227] In some embodiments and as Fig.15 1500, the surface of the tissue 104 is heated fractionally. In some embodiments, heat is applied by a plurality of pulses (e.g., of the same level of heat or joules) arranged at predetermined intervals (e.g., of equal or varying lengths), and the imager 108 of the thermal imaging system 100 acquires continuous thermal images throughout an increasing portion 1502 of the acquired thermal curve 1500.

[0228] exist Fig.15 In the exemplary embodiment shown, three heating pulses are applied at three time points tP1, tP2, and tP3, resulting in a stepped growth portion 1502 of the curve 1500 having three fractions ΔT1, ΔT2, and ΔT3. A potential advantage of fractional heating is that fractions of the growth portion 1502 are analyzed rather than the entire growth portion 1500, thereby allowing for increased resolution and accuracy of abnormal cell identification. In some embodiments, differences between tissue states or types are expressed in differences within only one of the fractions ΔT1, ΔT2, and ΔT3, thereby providing higher resolution of tissue type signature patterns and increasing accuracy and specificity of tissue type or state identification.

[0229] 3D Heating

[0230] Reference now Fig.16A and Fig. 16B , which is a simplified cross-sectional illustration of heat distribution within a portion of a tissue surface according to some embodiments of the present invention. In some embodiments, a volume of tissue 1602 below the surface of tissue 104 is heated along plane 1604 using a three-dimensional heating system, such as ultrasound, laser, IR or RF radiation applied at varying frequencies along line 1650 disposed on one side of suspected abnormal tissue 1606 in a direction indicated by arrow 1675 from the surface into deeper tissue.

[0231] like Fig.16A and Fig. 16B As shown, heat distribution within the portion 1602 below the surface of the tissue 104 occurs along line 1608. For clarity of explanation, heat distribution from the plane 1604 in a direction away from the suspected tissue section 1606 is ignored.

[0232] In some embodiments, the processor 110 of the imaging system 100 is configured to process multiple thermal images captured simultaneously or continuously along one or more planes at varying spatial orientations relative to the surface of the tissue 104 by a 3D thermal imaging system (e.g., an MRI, CT scanner, ultrasound transceiver, RF transceiver, or the like). In the exemplary embodiment shown in FIG. 16 , the multiple thermal images captured by the 3D thermal imaging system are acquired simultaneously or continuously along multiple planes that are spatially oriented parallel (plane 1608) and / or perpendicular (plane 1610) relative to the surface of the tissue 104.

[0233] like Fig. 16B As shown and in some embodiments, the processor 110 of the thermal imaging system 100 is configured to compile thermal behavior values ​​obtained from thermal images captured along multiple planes 1608 and / or plane 1610, and as explained in more detail elsewhere herein, extract information about one or more physiological or pathological thermal parameters associated with tissue identified as abnormal tissue cells 1606 in one or more of the obtained thermal images, and generate at least a three-dimensional contour of the suspected abnormal tissue 1606.

[0234] In some embodiments, the duality of at least a portion of the values ​​obtained by the processor 110 of the thermal imaging system 100 and the comparison between the obtained values ​​from the obtained images improves the accuracy and specificity of tissue type identification and localization within the tissue beneath the surface of the tissue 104. In some embodiments, the processor 110 of the thermal imaging system 100 is configured to superimpose the 3D contour of the suspected abnormal tissue 1606 onto the RGB 3D image of the tissue beneath the surface of the tissue 104 to assist the health professional in clearly and accurately identifying the boundaries of the suspicious cell clusters 904 within the tissue.

[0235] Throughout this application, various embodiments of the present invention may be presented in a range format. It should be understood that the description of the range format is only for convenience and brevity, and should not be interpreted as a rigid limitation on the scope of the present invention. Therefore, the description of the range should be considered to have clearly disclosed all possible sub-ranges and each value within the range. For example, the description of a range (such as from 1 to 6) should be considered to have clearly disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and a single number within the range, such as 1, 2, 3, 4, 5 and 6. This applies regardless of the breadth of the range.

[0236] Whenever a numerical range is indicated herein, it is intended to include any cited numeral (fractional or integer) within the indicated range. The phrases "range / range between a first indicated numeral and a second indicated numeral" and "range / range from a first indicated numeral to a second indicated numeral" are used interchangeably herein and are intended to include the first indicated numeral and the second indicated numeral and all fractions and integers therebetween.

[0237] In the specification and claims of the present application, each of the words "include," "comprises," and "has," and forms thereof, are not necessarily limited to the members of the list with which the words may be associated. Additionally, in the event of an inconsistency between the present application and any document incorporated by reference, the present application shall prevail.

[0238] Disclosed herein are methods and computer program products that can automatically construct (i.e., without human intervention) a list of relevant claims and supporting evidence given a subject matter under consideration (TUC). Thus, for example, a person can extract persuasive claims to support his or her case, as well as prepare for possible counterclaims that may be raised by the other party when discussing the TUC.

[0239] The present invention may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to perform aspects of the present invention.

[0240] A computer-readable storage medium may be a tangible device that can retain and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following items: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device (such as a punch card or a structure protruding in a groove, on which instructions are recorded), and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be understood as being a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse passing through an optical fiber cable), or an electrical signal transmitted by a wire.

[0241] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network, or downloaded to an external computer or external storage device. The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network, and forwards the computer-readable program instructions to be stored in a computer-readable storage medium in the corresponding computing / processing device.

[0242] The computer readable program instructions for performing the operations of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or a connection may be established with an external computer (for example, through the Internet using an Internet service provider). In some embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuits to perform aspects of the present invention.

[0243] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustration and / or block diagram and the combination of blocks in the flowchart illustration and / or block diagram can be implemented by computer-readable program instructions.

[0244] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct a computer, a programmable data processing device, and / or other equipment to function in a specific manner, so that the computer-readable storage medium having instructions stored therein includes an article of manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0245] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0246] The flow charts and block diagrams in the accompanying drawings illustrate the architecture, functions and operations of possible implementations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each frame in the flow chart or block diagram may represent a part of a module, section or instruction, which includes one or more executable instructions for realizing the specified (one or more) logical functions. In some alternative embodiments, the functions indicated in the frame may not occur in the order indicated in the figure. For example, two frames shown in succession may actually be executed substantially simultaneously, or sometimes these frames may be executed in reverse order, depending on the functions involved. It should also be noted that each frame of the block diagram and / or flow chart and the combination of the frames of the block diagram and / or flow chart can be implemented by a system based on dedicated hardware that performs a specified function or action or performs a combination of dedicated hardware and computer instructions.

[0247] Descriptions of various embodiments of the present invention have been given for purposes of illustration, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or technical improvements over technologies found on the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method comprising: controlling a heating source to heat at least one surface of at least a portion of the tissue from an initial base temperature within a predetermined first period of time; receiving thermal data associated with the tissue, wherein the thermal data is sampled during the first time period and during a second time period (404, 404'), during which the tissue is allowed to passively return to the base temperature; deriving a set of thermal distribution-related features associated with each of a plurality of locations within the tissue from the thermal data, wherein the set of thermal distribution-related features includes features calculated for the first time period and the second time period; segmenting the tissue into segments, each segment comprising one or more of the plurality of locations having a corresponding set of thermal distribution-related features; and An output is generated based on the set of thermal distribution related features, the output comprising at least one of: indicating the region of the tissue, and identifying individual tissue states in the region of the tissue.

2. The method of claim 1, wherein the thermal data is received from at least one of: thermal imaging, infrared sensor (IR sensor), mercury thermometer, resistance thermometer, thermistor, thermocouple, semiconductor-based temperature sensor, pyrometer, gas thermometer, laser thermometer, and ultrasound. 3 . The method of claim 2 , wherein the thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels of an image.

4. A method according to any of the preceding claims, wherein the set of thermal distribution related features represents at least one of the following items: tissue biological metabolic heat source, heat loss caused by blood perfusion, blood temperature, tissue density, specific heat, tissue thermal conductivity factor, tissue thermal conductivity coefficient, tissue thermal conductive surface area, tissue surface temperature, time-dependent thermal gradient between tissue and ambient temperature, ambient temperature, and heat source temperature.

5. The method of claim 1, wherein the deriving comprises calculating the set of heat distribution related features based at least in part on at least one thermal variable, and wherein the at least one thermal variable further comprises at least one of an ambient temperature and a heat source temperature.

6. A method according to claim 1, wherein the thermal data is received during a thermal disturbance, and wherein the thermal disturbance includes at least one of the following: actively achieving a temperature change from an initial temperature to a final temperature in at least a portion of the tissue, actively achieving a temperature change in at least a portion of the tissue for a specified time period, passively allowing a temperature change from an initial temperature to a final temperature in at least a portion of the tissue, and passively allowing a temperature change in at least a portion of the tissue for a specified time period.

7. The method according to claim 1 comprises extracting the set of thermal distribution related features based on at least some of the thermal data and thermal variables, wherein the thermal distribution related features are selected from a feature group including: features representing various derivative values ​​of the variables, features representing noise in the variables, features based on attenuation equations, features based on Fourier series, and related features based on differences of the features. 8 . The method of claim 1 , wherein the correspondence between the plurality of locations and the set of heat distribution related features is determined based at least in part on the difference values ​​of the features not exceeding a specified threshold.

9. The method of claim 8, further comprising determining a tissue state or type associated with each of the segments based at least in part on correlating the set of thermal distribution-related features associated with each of the segments.

10. The method of claim 9, wherein the correlating further comprises correlating the thermal distribution related features.

11. The method of claim 9, wherein the determining is performed by a machine learning classifier that is trained in a training phase on a training set that includes: (i) a plurality of thermal distribution-related feature sets, each thermal distribution-related feature set derived from thermal data sampled at each of a plurality of locations within a plurality of tissues during the first time period and the second time period; and (ii) a label associated with a state or type of each of the locations.

12. The method according to claim 11 also includes applying a trained machine learning classifier in an inference phase to the set of thermal distribution-related features derived from the thermal data sampled at the location of the tissue during the first time period and the second time period to determine the state or type of the location.

13. A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therein, the program code being executable by at least one hardware processor to: controlling a heating source to heat at least one surface of at least a portion of the tissue from an initial base temperature within a predetermined first period of time; receiving thermal data associated with tissue, wherein the thermal data is sampled during the first time period and during a second time period, during which the tissue is allowed to passively return to the base temperature; deriving a set of thermal distribution-related features associated with each of a plurality of locations within the tissue from the thermal data, wherein the set of thermal distribution-related features includes features calculated for the first time period and the second time period; segmenting the tissue into segments, each segment comprising one or more of the plurality of locations having a corresponding set of thermal distribution-related features; and An output is generated based on the set of thermal distribution related features, the output comprising at least one of: indicating the region of the tissue, and identifying individual tissue states in the region of the tissue.

14. The computer program product of claim 13, wherein the deriving comprises calculating the set of thermal distribution related features based at least in part on at least one thermal variable, and wherein the at least one thermal variable is indicative of a state or type of the tissue.

15. The computer program product of any one of claims 13 and 14, wherein the thermal data is received from at least one of the following: thermal imaging, infrared sensor (IR sensor), mercury thermometer, resistance thermometer, thermistor, thermocouple, semiconductor-based temperature sensor, pyrometer, gas thermometer, laser thermometer and ultrasound.

16. The computer program product of claim 13, wherein the thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels.

17. The computer program product of claim 13, wherein the tissue comprises actively or passively effecting a temperature change from an initial temperature to a final temperature on at least a portion of the tissue.

18. The computer program product of claim 13, wherein the tissue comprises achieving a temperature change over at least a portion of the tissue within at least one predetermined time period.

19. The computer program product of claim 13, wherein the deriving comprises calculating the set of thermal distribution-related features based at least in part on at least one thermal variable, and wherein the at least one thermal variable comprises at least one intrinsic tissue thermal parameter that affects the thermal behavior of cells.

20. The computer program product of claim 13, comprising computing a set of thermal distribution related features based on at least some of the thermal data and thermal variables.

21. A computer program product according to claim 20, wherein the feature is selected from the group of features including: features representing various derivative values ​​of the variable, features representing noise in the variable, features based on attenuation equations, features based on Fourier series, and correlation features based on differences of the features.

22. The computer program product of claim 20, wherein the correspondence between the plurality of locations and the set of heat distribution related features is determined based at least in part on the difference values ​​of all of the variables and the features not exceeding a specified threshold.

23. The computer program product of claim 13, wherein the deriving comprises calculating a set of thermal distribution-related features for each of the locations within the tissue based at least in part on at least one thermal variable.

24. A system comprising: Heating source; a thermal sensor configured to sample thermal data from the tissue; as well as A processor, the processor being configured to: controlling the heating source to heat at least one surface of at least a portion of the tissue from an initial base temperature within a predetermined first period of time; receiving thermal data associated with the tissue, wherein the thermal data is sampled during the first time period and during a second time period, during which the tissue is allowed to passively return to the base temperature; deriving a set of thermal distribution-related features associated with each of a plurality of locations within the tissue from the thermal data, wherein the set of thermal distribution-related features includes features calculated for the first time period and the second time period; segmenting the tissue into segments, each segment including one or more locations of the plurality of locations having a corresponding set of thermal distribution-related features; as well as An output is generated based on the set of features, the output comprising at least one of indicating the section of the tissue and identifying respective tissue states in the section of the tissue.

25. The system of claim 24, wherein the system comprises a heating source or a cooling source directed at at least the surface of the tissue and configured to actively heat or cool the tissue.

26. The system of claim 24, wherein the deriving comprises calculating the set of thermal distribution related features based at least in part on at least one thermal variable, and wherein the at least one thermal variable is indicative of a state or type of the tissue.

27. The system of claim 24, wherein the thermal data is received from at least one of: thermal imaging, infrared sensor (IR sensor), mercury thermometer, resistance thermometer, thermistor, thermocouple, semiconductor-based temperature sensor, pyrometer, gas thermometer, laser thermometer, and ultrasound.

28. The system of claim 24, wherein the thermal data is received by thermal imaging, and wherein the locations comprise pixels or voxels.

29. The system of claim 24, wherein the tissue comprises actively or passively effecting a temperature change from an initial temperature to a final temperature on at least a portion of the tissue.

30. The system of claim 24, wherein the tissue comprises achieving a temperature change on at least a portion of the tissue within at least one predetermined time period.

31. The system of claim 24, wherein the deriving comprises calculating the set of thermal distribution-related features based at least in part on at least one thermal variable, and wherein the at least one thermal variable comprises at least one intrinsic tissue thermal parameter that affects thermal behavior of cells.

32. The system of claim 24, comprising calculating a set of thermal distribution related features based on at least some of the thermal data and thermal variables.

33. A system according to any one of claims 32, wherein the feature is selected from a feature group including: features representing various derivative values ​​of the variable, features representing noise in the variable, features based on attenuation equations, features based on Fourier series, and correlation features based on differences of the features.

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