globally processing a plurality of light emission images to map and / or segment them

Abstract

A technical solution for imaging one or more body parts of a patient in medical applications is proposed. One corresponding method includes determining a mapping function that maps a global range of values ​​from multiple luminescent images of the body part to a (common) dynamic range of a display. The luminescent images are mapped by transforming their values ​​according to the mapping function. The resulting mapped luminescent images are then displayed together on the display. Alternatively or additionally, the method includes providing at least one (common) segmentation threshold for a starting image equivalent to the luminescent image or the mapped luminescent image. The starting image is segmented based on a comparison of its values ​​with the segmentation threshold. A computer program for implementing the method and a corresponding computer program product are also proposed. Furthermore, a corresponding computing device and an imaging system including the same are proposed. Surgical methods, diagnostic methods, and treatment methods based on the method are also proposed.

Description

Technical Field

[0001] This disclosure relates to imaging applications. More specifically, this disclosure relates to the processing of luminescent images in medical applications. Background Technology

[0002] The background of this disclosure will be introduced below, and techniques relevant to its context will be discussed. However, even when the discussion involves documents, actions, artifacts, etc., it does not imply or represent that the techniques discussed are part of the prior art or general common sense in the field related to this disclosure.

[0003] Images (in digital form) are generally used to provide a visual representation of a scene that includes one or more physical objects. In particular, in devices used for medical applications, imaging techniques are used to examine parts of a patient's body (usually in a largely non-invasive manner, even if they are not directly visible).

[0004] A particular imaging technique being increasingly considered is luminescence imaging, especially fluorescence imaging. Luminescence imaging is based on the phenomenon of light emission, which involves luminescent substances emitting light when excited by anything other than heating; specifically, fluorescence occurs in fluorescent substances (called fluorophores) that emit light (fluorescence) when irradiated. An image of a body part defined by fluorescence emitted from different locations within the body (a fluorescence image) represents the fluorophores present therein. For example, a fluorescent agent (a specific molecule adapted to reach a desired target, such as a lesion, like a tumor) can be administered to a patient and then immobilized thereon in fluorescence molecular imaging (FMI) applications. Representing the (immobilized) fluorescent agent in the fluorescence image then aids in the identification (and quantification) of the corresponding target. This information can be used in a variety of medical applications, such as in surgical applications to identify the margins of a lesion to be removed, diagnostic applications to detect / monitor lesions, and therapeutic applications to delineate lesions to be treated.

[0005] However, correctly identifying lesions remains very challenging because it is adversely affected by a number of obstacles.

[0006] In particular, different fluorescence images of the same body part acquired during related medical procedures can have different dynamic ranges. This makes the identification of fluorescent agents both extremely difficult in absolute terms (for their quantification) and in relative terms (in different fluorescence images).

[0007] Therefore, fluorescence images are typically segmented (divided into segments defined by their substantially homogeneous parts) to distinguish the fluorescent agent (and consequently the corresponding target) from the rest of the body part. For this purpose, the value of each fluorescence image (representing the corresponding location of the body part) is compared to a segmentation threshold calculated based on the statistical distribution of the fluorescence image values. However, interfering light (e.g., due to surgical instruments, hands, surgical tools, surrounding body parts, and background material) can skew the statistical distribution of the fluorescence image values ​​(increasing or decreasing it), thus affecting the segmentation threshold.

[0008] This involves the risk of misclassifying the location of body parts. For example, in surgical applications, this creates uncertainty in the accurate identification of lesion margins (with the risk of incomplete lesion removal or excessive removal of healthy tissue). In diagnostic applications, this adversely affects lesion identification and / or quantification, potentially leading to misinterpretations (with the risk of false positives / negatives and incorrect follow-up). In therapeutic applications, this adversely affects the delineation of the lesion to be treated (with the risk of reduced treatment effectiveness or damage to healthy tissue). All of these are detrimental to the patient's health. Summary of the Invention

[0009] A simplified overview of this disclosure is presented herein to provide a basic understanding of it; however, the sole purpose of this summary is to introduce some concepts of the disclosure in a simplified form as a prelude to its more detailed description below, and it shall not be construed as an identification of its key elements or as a delineation of its scope.

[0010] Generally speaking, this disclosure is based on the idea of ​​processing fluorescence images at a global level.

[0011] In particular, one aspect provides a method for imaging one or more body parts of a patient in a medical application. A mapping function is determined that maps a global range of values ​​from multiple luminescent images of the body part to the dynamic range of a display. The luminescent images are mapped by transforming the values ​​of the luminescent images according to the mapping function. The resulting mapped luminescent images are then displayed together on the display.

[0012] Alternatively or alternatively, at least one segmentation threshold is determined based on the values ​​of all luminescent images to separate the values ​​of all luminescent images into a number of groups. The luminescent images are then segmented individually based on a comparison of their values ​​with the segmentation threshold.

[0013] On the other hand, a computer program for implementing the method is provided.

[0014] On the other hand, a corresponding computer program product is provided.

[0015] On the other hand, a computing device for implementing the method is provided.

[0016] On the other hand, an imaging system including the computing device is provided.

[0017] On the other hand, a corresponding surgical method is provided.

[0018] On the other hand, it provides a corresponding diagnostic method.

[0019] On the other hand, a corresponding treatment product is provided.

[0020] More specifically, one or more aspects of this disclosure are set forth in the independent claims and their advantageous features are set forth in the dependent claims, using the wording of all claims incorporated herein by reference (with any advantageous features provided by reference to any particular aspect applicable to each of the other aspects). Attached Figure Description

[0021] The technical solutions disclosed herein, and their further features and advantages, will be best understood by reference to the following detailed description, which is given purely by non-limiting indication and should be read in conjunction with the accompanying drawings (wherein, for simplicity, corresponding elements are indicated by the same or similar references, and their explanations are not repeated, and the name of each entity is generally used to indicate both its type and its attributes, such as value, content, and representation). In particular:

[0022] Figure 1 A schematic block diagram of an imaging system that can be used to implement technical solutions according to embodiments of the present disclosure is shown.

[0023] Figures 2A to 2D The general principles of the technical solutions according to embodiments of the present disclosure are illustrated.

[0024] Figures 3A to 3B The general principle of a technical solution according to another embodiment of this disclosure is shown.

[0025] Figure 4 The main software components that can be used to implement the technical solution according to embodiments of the present disclosure are shown.

[0026] Figures 5A to 5B An activity diagram illustrating the flow of activities related to the implementation of technical solutions according to embodiments of the present disclosure is shown, and

[0027] Figures 6 to 9 Different examples of the application of technical solutions according to embodiments of this disclosure are shown. Detailed Implementation

[0028] For specific references Figure 1The diagram shows a schematic block diagram of an imaging system 100 that can be used to implement technical solutions according to embodiments of the present disclosure.

[0029] Imaging system 100 enables imaging of a scene encompassed within its field of view 103 (defined by the imaging system 100 within a portion of the world within its sensitive solid angle). For example, imaging system 100 is used to apply fluorescence imaging techniques to assist surgeons in fluorescence-guided surgery (FGS), particularly fluorescence-guided resection (FGR) involving tumors. In this specific case, the scene involves a patient 106 undergoing the surgical procedure, who has previously been given a fluorescent agent (e.g., suitable for accumulation in a tumor). The scene includes body part 109 of patient 106, where a surgical cavity 112 (e.g., a small skin incision in minimally invasive surgery) has been opened to expose the tumor 115 to be removed. The scene may also include one or more foreign objects different from the surgical cavity 112 (not shown), such as surgical instruments, hands, surgical tools, surrounding body parts, background material, etc.

[0030] The imaging system 100 has an imaging probe 118 for acquiring images of the field of view 103 and a central unit 121 for controlling its operation.

[0031] Starting with imaging probe 118, it includes the following components: Excitation light source 124 and white light source 127 generate excitation light and white light, respectively. The wavelength and energy of the excitation light are suitable for exciting fluorophores (such as near-infrared (NIR) type), while white light is substantially colorless to the human eye (such as containing all wavelengths of the spectrum visible to the human eye at the same intensity). Transmission optics 130 and 133 transmit the excitation light and white light to the (same) field of view 103, respectively. Collection optics 136 collect light from the field of view 103 (in an epi-illumination geometry). The collected light includes fluorescence emitted by any fluorophores present in the field of view 103 (illuminated by the excitation light). In practice, when a fluorophore absorbs the excitation light, it enters an excited (electronic) state; the excited state is unstable, so the fluorophore quickly decays from it to the ground (electronic) state, thus emitting fluorescence whose intensity depends primarily on the number of fluorophores irradiated (since energy is dissipated as heat in the excited state, fluorescence is emitted at a characteristic wavelength longer than the wavelength of the excitation light). Furthermore, the collected light includes visible light (in the visible spectrum) reflected by any object present in the field of view 103 (illuminated by white light). A beam splitter 139 divides the collected light into two channels. For example, the beam splitter 139 is a dichroic mirror that transmits and reflects (or vice versa) the collected light at wavelengths above and below a threshold wavelength between the visible and fluorescence spectra, respectively. In the (transmission) channel of the beam splitter 139, where a portion of the light collected in its spectrum defines fluorescence, an emission filter 142 filters the fluorescence to remove any excitation light (that may be reflected by the field of view 103) and any ambient light (that may be generated by inherent fluorescence). A fluorescence camera 145 (e.g., of the EMCCD type) receives the fluorescence from the emission filter 142 and generates a corresponding fluorescence (digital) image representing the distribution of fluorophores in the field of view 103. In the other (reflection) channels of beam splitter 139, where a portion of the light collected in its spectrum defines visible light, a (e.g., a CCD-type) reflection (or imaging) camera 148 receives the visible light and generates a corresponding reflected (digital) image representing what the human eye can see in field of view 103.

[0032] Turning to the central unit 121, it includes several units interconnected therebetween via a bus structure 151. Specifically, one or more microprocessors (μP) 154 provide the logical capabilities of the central unit 121. Non-volatile memory (ROM) 157 stores the basic code for booting the central unit 121, and volatile memory (RAM) 160 is used as working memory by the microprocessor 154. The central unit 121 is provided with a mass storage 163 (e.g., a solid-state drive (SSD)) for storing programs and data. Furthermore, the central unit 121 includes multiple controllers 166 or input / output (I / O) units for peripheral devices. Specifically, controller 166 controls the excitation light source 124, white light source 127, fluorescence camera 145, and reflection camera 148 of imaging probe 118; in addition, controller 166 controls other peripheral devices as a whole, indicated by reference numeral 169, such as one or more monitors for displaying fluorescence / reflection images, a keyboard for inputting information / commands, a trackball for moving a pointer on the monitor, a drive for reading / writing removable storage units (such as USB keys), and a network interface card (NIC) for connecting to a (communication) network (such as a local area network (LAN)).

[0033] Now for reference Figures 2A to 2D This illustrates the general principles of the technical solutions according to embodiments of the present disclosure.

[0034] from Figure 2A To begin, multiple fluorescence images of one or more body parts of the patient are provided (each fluorescence image includes multiple values ​​representing the corresponding location of the body part). For example, the fluorescence images relate to the same body part imaged from different (imaging) directions, different regions of the same body part, or different body parts. In the specific application discussed, the fluorescence images relate to body part 109 undergoing the surgical procedure, acquired from six directions in the example shown in the figure.

[0035] Go to Figure 2B Determine the global range of values ​​for all fluorescence images; this global range spans from the lowest to the highest value of all fluorescence images (which may be filtered to remove outliers).

[0036] Go to Figure 2C The mapping function is determined. The mapping function maps the global range to the dynamic range (e.g., linearly) used to display the fluorescence image; the dynamic range (which is the same for all fluorescence images) relates to the display used for this purpose (the monitor of the imaging system not shown in the figure in the application discussed).

[0037] Go to Figure 2DEach fluorescence image is mapped to a dynamic range; for this purpose, each value of the fluorescence image is transformed according to the mapping function. The resulting (mapped) fluorescence images are then displayed together (on a monitor of an imaging system not shown in the figure in the application discussed).

[0038] The above technical solution balances the representation of body parts in different fluorescence images.

[0039] In fact, this normalizes the mapped fluorescence images to the same dynamic range. This helps in the quantitative evaluation of the mapped fluorescence images (absolute values); furthermore, it facilitates comparisons between mapped fluorescence images (relative values).

[0040] In particular, in the examples discussed, the identification of (fixed) fluorescent agents and, consequently, the corresponding targets was significantly improved.

[0041] Now for reference Figures 3A to 3B This illustrates the general principle of a technical solution according to another embodiment of the present disclosure.

[0042] from Figure 3A Initially, a segmentation threshold (or larger) is provided; this threshold is the same for all fluorescence images (either in their original form or after being mapped to dynamic range). For example, in one implementation, the segmentation threshold is determined globally based on all fluorescence images; specifically, it is determined based on the statistical distribution of the values ​​of all fluorescence images to separate the values ​​of all fluorescence images from a certain number of distinct groups (with substantially uniform characteristics). In the specific application discussed, the segmentation threshold separates the values ​​of all fluorescence images into two groups, corresponding to detected and undetected fluorescent agents, respectively.

[0043] Go to Figure 3B Each fluorescence image is segmented into the same number of distinct segments representing corresponding regions of body parts (with substantially uniform characteristics); segments are determined by comparing the values ​​of the fluorescence image with the same segmentation threshold. In the specific application discussed, each fluorescence image is segmented into (detection) segments where fluorescent agents are detected and (non-detection) segments where fluorescent agents are not detected (thus representing tumors and healthy tissue, respectively).

[0044] The above technical solution significantly improves the accuracy of segmentation of (potentially mapped) fluorescence images.

[0045] In fact, since the values ​​of all fluorescence images are considered at this point, the impact of some possible anomalies is mitigated. Therefore, the statistical distribution of the values ​​of all fluorescence images is less skewed by anomalies; this reflects a smaller deviation in the segmentation threshold.

[0046] Specifically, in the examples discussed, any interfering light (such as that caused by foreign objects) adversely affects the segmentation threshold to a lesser extent. As a result, the risk of misclassifying the location of body parts is significantly reduced.

[0047] The mapping and / or segmentation of fluorescence images mentioned above help identify the edges of lesions to be removed in surgical applications, identify / quantify lesions in diagnostic applications, and delineate lesions to be treated in therapeutic applications. All of these have beneficial effects on patient health.

[0048] Now for reference Figure 4 This illustrates a main software component that can be used to implement a technical solution according to an embodiment of the present disclosure.

[0049] All software components (programs and data) are represented as a whole by reference numeral 400. Software component 400 is typically stored in mass storage and, while the program is running (at least partially), is loaded into the working memory of the central unit of the imaging system along with the operating system and other applications that are not directly related to the technical solutions of this disclosure (and are therefore omitted in the figures for simplicity). Programs are initially installed into mass storage, for example, from removable storage units or from a network. In this respect, each program may be a module, fragment, or portion of code, including one or more executable instructions for implementing a specific logical function.

[0050] Specifically, the acquirer 403 drives components of an imaging system dedicated to acquiring fluorescence / reflection images of the field of view appropriately illuminated for this purpose (during the surgical procedure in the example discussed). The acquirer 403 writes to image libraries 406 and 409, which respectively contain corresponding sets of fluorescence and reflectance images acquired during the ongoing imaging procedure. Fluorescence image libraries 406 and 409 include corresponding entries for each pair of fluorescence and reflectance images (acquired simultaneously in the same manner). The entries in the fluorescence / reflectance image libraries 406 and 409 store bitmaps of fluorescence / reflectance images, defined by a matrix of cells (e.g., having 512 rows and 512 columns), each cell containing a pixel value, where a pixel is a basic image element representing a corresponding location in the field of view; each (fluorescence) value of the fluorescence image defines the brightness of the pixel as varying with the intensity of fluorescence emitted at that location, whereas each (reflectivity) value of the reflectance image defines the brightness of the pixel as varying with the intensity of visible light reflected at that location (e.g., from black to white in grayscale representation). The entries may also store descriptive labels for the fluorescence / reflectance images; for example, descriptive labels indicating the imaging arrangement (such as its imaging direction, region, and / or body part) in which the fluorescence / reflectance images were acquired.

[0051] Optionally, the comparison image library 412 contains one or more comparison (fluorescence) images corresponding to comparison entries. For example, the comparison images may include a reference (fluorescence) image or more representing a reference device having one or more locations containing a known concentration of fluorescent agent (e.g., a container filled with liquid containing different concentrations of fluorescent agent). Alternatively or additionally, the comparison images may include one or more assessment (fluorescence) images of the same type of body part (e.g., in a healthy state and with the same lesion) from different patients. The comparison image library 412 has an entry for each comparison image. This entry stores a bitmap of the comparison image defined by a matrix of cells (having the same or different dimensions relative to the fluorescence image), each cell containing the (comparison) value of a corresponding pixel. The entry may also store descriptive labels for the comparison images; for example, descriptive labels indicating the concentration of fluorescent agent in the reference image, the type and / or condition of the body part in the assessment image.

[0052] Configuration information store 415 stores configuration information for the imaging process. The configuration information includes indications of different imaging arrangements from which fluorescence / reflectance images can be obtained; for example, imaging arrangements are imaging directions for the same body part (such as all anatomical directions (i.e., anterior, posterior, superior, inferior, medial, lateral)), different regions of the same body part (such as healthy tissue and pathological tissue), different body parts (such as corresponding organs), etc. The configuration information includes indications of the dynamic range (used for mapping fluorescence images). The configuration information includes indications of the parametric function used to determine the mapping function, which depends on parameters defined by the global range and dynamic range; for example, the parametric function is of linear type.

[0053] y = m*x + Ld - m*Lg

[0054] m=(Hd-Ld) / (Hg-Lg),

[0055] Where Lg and Hg are the lower and upper limits of the global range, respectively, and Ld and Hd are the lower and upper limits of the dynamic range, respectively. The acquirer 403 reads the configuration information store 415 (specifically, the imaging arrangement). The configurator 418 exposes a user interface for updating the configuration information. For example, the user interface includes checkboxes for selecting an imaging arrangement (among all available arrangements) and sliders for changing the lower and upper limits of the dynamic range. The configurator 418 reads from and writes to the configuration information store 415 (specifically, the imaging arrangement and dynamic range).

[0056] Mapper 421 accordingly determines the global range (of values ​​for all fluorescence images and possibly one or more comparison images), and then accordingly determines the mapping function (for mapping the fluorescence images / comparison images). Mapper 421 reads the fluorescence image library 406, the comparison image library 412, and the configuration information library 415 (specifically, the dynamic range and parameter functions). Furthermore, mapper 421 writes to the mapping function library 424. The mapping function library 424 stores indications of mapping functions (consisting of instances of parameter functions defined by the actual global range and dynamic range). Mapper 421 further maps the fluorescence images / comparison images according to the mapping functions. Mapper 421 writes to the mapping function library 427. The mapping image library 427 stores a set of mapped (fluorescence) images corresponding to fluorescence images and a set of possible mapped (comparison) images corresponding to comparison images. Each mapped fluorescence image / comparison image is formed by a bitmap, which is defined by a matrix of cells of the same size as the fluorescence image (for mapped fluorescence images) or the comparison image (for mapped comparison images), each cell containing the (mapped) value of the corresponding pixel.

[0057] Thresholder 430 determines a segmentation threshold for segmenting (potentially mapped) fluorescence images. Thresholder 430 reads from fluorescence image storage 406 and / or mapped image storage 427 (specifically, mapped fluorescence images). Furthermore, thresholder 430 writes to segmentation threshold storage 433. Segmentation threshold storage 433 stores segmentation thresholds. Segmenter 436 segments the initially provided fluorescence image or one after its mapping (based on the segmentation threshold). Segmenter 436 reads from fluorescence image storage 406 and / or mapped image storage 427 (specifically, mapped fluorescence images), and it also reads from segmentation threshold storage 433. Segmenter 436 may also display a user interface for manually (e.g., by means of a slider) changing the segmentation threshold. Furthermore, segmenter 436 writes to segmentation mask storage 439. Segmentation mask storage 439 stores a set of segmentation masks corresponding to the fluorescence images. Each segmentation mask is formed by a matrix of cells of the same size as the fluorescence image, each cell containing a segmentation label indicating the classification of the corresponding location; in the case of only one segmentation threshold and thus only two (detected / undetected) segments, the segmentation label is a (binary) segmentation flag, for example, the segmentation flag is valid when the location belongs to a detected segment (e.g., at a logic value of 1), and invalid when the location belongs to an undetected segment (e.g., at a logic value of 0).

[0058] Processor 442 generates a set of processed (fluorescent) images corresponding to fluorescence images and a possible set of processed (comparative) images corresponding to comparison images. The processed fluorescence images can be mapped fluorescence images, segmented (fluorescent) images obtained by resetting the values ​​of corresponding non-detected segments of (possibly mapped) fluorescence images (to black), or overlaid images obtained by superimposing segmented images onto corresponding reflectance images; the processed comparison images can be mapped comparison images or (original) comparison images. Processor 442 reads one or more of the fluorescence image library 406, reflectance image library 409, comparison image library 412, mapped image library 427, and segmentation mask library 439. Furthermore, processor 442 writes to the processed image library 445. The processed image library 445 includes entries for each processed (fluorescent / comparative) image. This entry stores a bitmap of the processed fluorescence image / comparison image, defined by a matrix of cells of the same size as the fluorescence image (for the processed fluorescence image) or the comparison image (for the processed comparison image), each cell containing the (processed) value of the corresponding pixel. This entry also stores descriptive labels for the corresponding fluorescence image / comparison image. Furthermore, in the case of a processed fluorescence image, this entry also stores a ranking index (indicating the ability of the processed fluorescence image to detect fluorescent agents) calculated by the processor 442. The display driver 448 drives the display (i.e., the monitor of the imaging system) to display the processed (fluorescence / comparison) image (possibly along with corresponding descriptive labels and / or ranking indices) on the display. The display driver 448 reads the processed image storage 445.

[0059] Now for reference Figures 5A to 5B An activity diagram is shown illustrating the flow of activities related to the implementation of the technical solutions according to embodiments of the present disclosure.

[0060] Specifically, this activity diagram represents an exemplary process that can be used to image one or more body parts of a patient in a medical application (during the surgical procedure in the example discussed) using method 500. In this regard, each block may correspond to one or more executable instructions for implementing a specified logical function on the imaging system.

[0061] Prior to the surgical procedure, a healthcare operator (e.g., a nurse) administers a fluorescent agent to the patient. The fluorescent agent (e.g., indocyanine green, methylene blue, etc.) is adapted to reach a specific (biological) target (such as the tumor to be removed) and remain substantially immobilized therein. This outcome can be achieved by using a non-targeted fluorescent agent (adapted to accumulate in the target without any specific interaction with it, such as through passive accumulation) or a targeted fluorescent agent (adapted to attach to the target by means of a specific interaction with it, such as by incorporating a target-specific ligand into a formulation of the fluorescent agent based, for example, on the chemical binding properties and / or physical structure, vascular properties, metabolic characteristics, etc., that enable it to interact with different tissues). For example, the fluorescent agent is administered intravenously to the patient in a given dose (using a syringe). The fluorescent agent thus circulates within the patient's vascular system until it reaches and binds to the tumor; conversely, any remaining (unbound) fluorescent agent is removed from the blood pool. After a waiting period (e.g., from a few minutes to 24–72 hours) allowing the fluorescent agent to accumulate in the tumor and be flushed out from the rest of the patient's body, the surgical procedure can begin. Therefore, the (healthcare) operator turns on the imaging system.

[0062] In response, the process begins in the black starting circle 503 and proceeds to box 506. Once the operator (e.g., using the imaging system's keypad) inputs a start command into the imaging system, in box 509, the acquirer activates the excitation light source and the white light source to illuminate the field of view.

[0063] Then, a loop is executed to acquire fluorescence / reflectance images from different (selected) imaging arrangements. The loop begins at box 512, where the acquirer retrieves an indication of the (current) imaging arrangement from the configuration information store (starting from the first in corresponding order). In box 515, the acquirer displays a message on the monitor prompting the operator to place the imaging probe near the area where the surgeon has an open surgical cavity according to the imaging arrangement (e.g., reciprocating according to the corresponding imaging direction to image the corresponding area of ​​the body part or to image the corresponding body part). Once the desired imaging arrangement is established, in box 518, the operator (e.g., using the imaging system's keyboard) enters a confirmation command into the imaging system; in response, the acquirer simultaneously acquires the (new) fluorescence and reflectance image pair and adds them to the corresponding store; in this way, the fluorescence and reflectance images are acquired substantially simultaneously, and they provide different representations of the same field of view (in terms of fluorescence and visible light, respectively) that are spatially coherent (i.e., there is a predictable correlation between their pixels until perfect consistency). In box 521, the acquirer verifies whether the final imaging arrangement has been considered. If not, the process returns to box 512 and repeats the same operation for the next imaging arrangement. Conversely (once all imaging arrangements have been considered), the loop exits by proceeding down to box 524. At this point, if desired, one or more comparison images are selected from all available images in the corresponding repository; for example, in response to a prompt to manually select comparison images based on configuration parameters or by limiting the comparison images by default (up to all images).

[0064] In box 527, the activity flow branches according to the operating mode of the imaging system (e.g., manually selected at the start of the imaging process, limited by configuration parameters, or uniquely available). Specifically, boxes 539 to 554 are executed when mapping a fluorescence image, and boxes 557 to 572 are executed when segmenting a fluorescence image; in both cases, the process converges again in box 575.

[0065] Now consider box 530 (mapping). The mapper can optionally preprocess the fluorescence images by performing one or more (linear or nonlinear) preprocessing operations, such as median filtering, low-pass filtering, noise suppression algorithms, pixel-wise nonlinear compensation using calibration data, etc. These preprocessing operations can be applied globally (to all fluorescence images), independently (to each fluorescence image), or locally (to different regions of each fluorescence image). In any case, at box 533, the mapper determines the global range of all (possibly preprocessed) fluorescence images and (selected) comparison images (if any, retrieved from the corresponding repository). For this purpose, the mapper scans the values ​​of all fluorescence / comparison images (in any arbitrary order) to determine their minimum and maximum values, which respectively define the lower and upper limits of the global range. This operation can be applied indiscriminately to all values ​​or by filtering out outliers. For example, the mapper calculates the lower percentile (e.g., 0.2–2.0, such as 1.0) and upper percentile (e.g., 98.0–99.8, such as 99.0) for the values ​​of all fluorescence images / comparison images; then, the mapper ignores values ​​below the lower percentile or above the upper percentile. In box 536, the mapper determines the mapping function. The mapping function is defined by an instance of a parametric function (also retrieved from the configuration information store) evaluated according to the (just determined) global range and the dynamic range (retrieved from the configuration information store). Then, at box 539, a loop is entered where the mapper considers the (current) fluorescence images / comparison images (starting from the first in any arbitrary order). At box 542, the mapper generates a mapped fluorescence image / comparison image corresponding to the fluorescence image / comparison image by setting each value of the mapped fluorescence image / comparison image to the result of the mapping function applied to the corresponding value of the fluorescence image / comparison image; the mapper then saves the thus obtained mapped fluorescence image / comparison image to the corresponding store. At box 545, the mapper verifies whether the last fluorescence image / comparison image has been considered. If not, the process returns to box 539 and repeats the same operation for the next fluorescence image / comparison image. Conversely (once all fluorescence images / comparison images have been considered), the loop exits by proceeding down to box 548. At this point, if necessary, the processor sets the processed comparison image (corresponding to the one in the storage) to be equal to the mapped comparison image (adding a descriptive label to the corresponding comparison image (from its storage)). Depending on the operating mode of the imaging system, the activity flow branches further at box 551. If the mapped fluorescence image also needs to be segmented, the process continues to box 557 (described below). Conversely, at box 554, the processor sets the processed fluorescence image (corresponding to the one in the storage) to be equal to the mapped fluorescence image (adding a descriptive label to the corresponding fluorescence image (from its storage)). The activity flow then proceeds down to box 575.

[0066] Now consider box 557 (segmentation). The thresholder determines a segmentation threshold based on the values ​​of all relevant images (retrieved from the corresponding storage) that are fluorescent images when the point is directly reached from box 527, or mapped fluorescent images when the point is reached from box 551 after a mapped fluorescent image (in both cases, hereinafter referred to as the starting image). For example, the segmentation threshold is calculated by applying the Otsu algorithm to minimize the within-group variance of the values ​​of all starting images among the groups separated by the segmentation threshold. Then, a loop is entered at box 560, where the segmenter considers the (current) starting images (starting from the first in any arbitrary order). At box 563, the segmenter generates a segmentation mask corresponding to the starting image by enabling or deactivating each segmentation flag of the segmentation mask when the corresponding value of the starting image is (possibly strictly) higher or lower than the segmentation threshold (meaning the corresponding position belongs to a detected segment or a non-detected segment, respectively); the segmenter then stores the segmentation mask thus obtained in the corresponding storage. In box 566, the processor generates a corresponding processed fluorescence image based on the starting image, the segmentation mask, and possible reflection images (retrieved from its storage). For example, when the processed fluorescence image is a segmentation image, it is generated by resetting each value of the starting image (to black) when the corresponding segmentation flag of the segmentation marker is invalid (non-detection segment); alternatively, when the processed fluorescence image is a superimposed image, it is generated by setting each value of the segmentation marker to a color representation equal to the corresponding value of the starting image (e.g., red with increased brightness) when the corresponding segmentation flag of the segmentation marker is valid (detection segment) or setting each value of the segmentation marker to a corresponding (black and white) value equal to the reflection image when the corresponding segmentation flag of the segmentation marker is invalid (non-detection segment). In either case, the processor then saves the processed fluorescence image thus obtained (with a descriptive label added to the starting image) to the corresponding storage. At box 569, the segmenter verifies whether the last starting image has been considered. If not, the process returns to box 560 and repeats the same operation for the next starting image. Conversely (once all starting images have been considered), the loop exits by proceeding down to box 572. At this point, if there is a comparison image that has not yet been mapped, the processor sets the processed comparison image (in the corresponding repository) to be equal to the comparison image (with the corresponding descriptive label added (from its repository)). The activity then flows down to box 575.

[0067] Referring now to box 575, the process enters a loop for calculating the ranking index of the processed fluorescence images. The loop begins with the processor considering the (current) processed fluorescence images (starting from the first in any arbitrary order). At box 578, the processor calculates the ranking index of the processed fluorescence images. Specifically, in the case of segmented / overlayed images, the ranking index indicates the segmentation quality of the corresponding (possibly mapped) fluorescence image; for example, the ranking index is set as the average of the values ​​of the segmented images corresponding to valid segmentation flags (detected segments) in the corresponding segmentation mask divided by the average of the values ​​of the segmented images corresponding to invalid segmentation flags (non-detected segments) in the corresponding segmentation mask. Alternatively, in the case of mapped fluorescence images, the ranking index indicates the mapping quality of the corresponding fluorescence image; for example, the ranking index is set as the average of all values ​​of the mapped fluorescence image. The processor then adds the ranking index to the processed fluorescence image (corresponding to the storage). At box 581, the processor verifies whether the last processed fluorescence image has been considered. If not, the process returns to box 575, and the same operation is repeated for the next processed fluorescence image. Instead (once all the processed fluorescence images have been considered), exit the loop by going down to box 584.

[0068] At this point, the display driver displays the processed fluorescence image and a processed comparative image (if any) along with their descriptive labels on the imaging system's monitor (e.g., in the same window). For example, in this way, the surgeon can determine the (optimal) processed fluorescence image that provides the most useful representation of the body site (e.g., the highest fluorescence dose). In particular, when the processed fluorescence image is a mapped fluorescence image, their shared dynamic range allows for direct comparison of detected fluorophores. Alternatively or alternatively, when the processed fluorescence image is a segmented / overlay image, highlighting the detected fragments helps in assessing the detected fluorophores, which, in the case of an overlay image, are further contextualized within the anatomical structure of the body site. In any case, ranking indices associated with the processed fluorescence image further aid in the identification of the optimal processed fluorescence image, thanks to the corresponding quantification of the processed fluorescence image's ability to detect fluorophores. For example, ranking indices can be displayed by their values, by corresponding labels or colors associated with their predefined ranges (e.g., distinctly positive, ambiguous, and distinctly negative, or red, yellow, and green, respectively). As a result, the surgeon can determine which (optimal) imaging orientation is most effective for the surgeon's procedure. Alternatively or alternatively, in this way, surgeons can compare fluorescent agents detected in different areas of the same body part in a patient (e.g., in healthy and diseased conditions) and / or in other body parts (e.g., in different related organs). Furthermore, the processed comparison images enable the inference of quantitative assessments of the detected fluorescent agents (especially when they are mapped together with fluorescent images); for example, when the comparison images include a reference image, this allows for comparison of the detected fluorescent agents with their known amounts; however, when the comparison images include an evaluation image, this allows for comparison of fluorescent agents detected in the same type of body part in other patients (under known conditions).

[0069] Then, based on the surgeon's selection, the activity flow branches at box 587. If the surgeon requests the operator to adjust the dynamic range, the operator updates it accordingly via the configurator's user interface at box 590 (e.g., by using the mouse to move the corresponding slider to change the lower and / or upper limit of the dynamic range). Finally, the configurator saves the updated version of the dynamic range to the configuration information store (by replacing its previous version). The process then returns to box 536 to repeat the same operation for the updated version of the dynamic range. If the surgeon alternatively requests the operator to adjust the segmentation threshold, the operator updates it accordingly via the segmenter's user interface at box 593 (e.g., by using the mouse to move the corresponding slider to increase / decrease it). The process then returns to box 560 to repeat the same operation for the updated version of the segmentation threshold. Otherwise, when the surgeon confirms that the process is complete, the activity flow ends at the concentric white / black stop circle 596 (after the acquirer turns off the excitation light source and the white light source).

[0070] The results of the above process can then be used for various purposes. For example, a further (standard) imaging procedure can be performed by repeatedly positioning the imaging probe and body part according to the thus determined optimal imaging orientation. This allows operation under optimized (imaging) conditions, which has a beneficial impact on the quality of the surgical procedure. Alternatively or alternatively, a (comparable) representation of the body part can be provided to the surgeon during and at the beginning of the surgical procedure, or a (comparable) representation of the body part can be provided during the surgical procedure and a representation of other areas or other body parts of the same body part can be provided at the beginning of the surgical procedure. This allows the surgeon to accurately track the progress of the surgical procedure.

[0071] Now for reference Figures 6 to 9 This illustrates different examples of the application of technical solutions according to embodiments of the present disclosure.

[0072] from Figure 6 Initially, two fluorescence images, 600a and 600b, of the same body part undergoing the surgical procedure were acquired from different imaging directions. In this case, comparing the fluorescence images 600a and 600b is very difficult because they have different dynamic ranges.

[0073] Go to Figure 7 As described above, corresponding mapped fluorescence images 700a and 700b are generated from these fluorescence images. Due to their common dynamic range, comparing the mapped fluorescence images 700a and 700b is much easier at this point.

[0074] Go to Figure 8Add reference image 800f. Reference image 800f shows different (known) concentrations of the fluorescent agent, increasing counterclockwise from the 12 o'clock position. This allows for comparison of the fluorescent agent shown in the mapped fluorescence images 700a and 700b with the known amount of the fluorescent agent shown in reference image 800f.

[0075] Go to Figure 9 As described above, corresponding segmented images 900a and 900b are generated from these mapped fluorescence images. Segmented images 900a and 900b, also shown together with reference image 800f, further aid in the evaluation of the detected fluorescent agents (i.e., comparison of the detected fluorescent agents between segmented images 900a and 900b and quantification of the detected fluorescent agents relative to reference image 800f).

[0076] Modification format

[0077] Naturally, those skilled in the art can apply numerous logical and / or physical modifications and alterations to this disclosure to meet local and specific requirements. More specifically, although this disclosure has been described with a degree of particularity with reference to one or more embodiments thereof, it should be understood that various omissions, substitutions, and changes in form and detail, as well as in other embodiments, are possible. In particular, different embodiments of this disclosure may even be implemented without the specific details (such as numerical values) set forth in the foregoing description to provide a more thorough understanding thereof; rather, well-known features may be omitted or simplified so as not to obscure the description with unnecessary detail. Furthermore, it is expressly expected that specific elements and / or method steps described in connection with any embodiment of this disclosure may be incorporated into any other embodiment for considerations of general design choice. Moreover, entries presented in the same group and in different embodiments, examples, or alternatives are not to be construed as being factually equivalent to each other (but are separate and autonomous entities). In all cases, each numerical value should be read as modified according to applicable tolerances; in particular, unless otherwise indicated, the terms “substantially,” “about,” “approximately,” etc., should be understood as within 10%, preferably within 5%, and more preferably within 1%. Furthermore, each range of numerical values ​​should be intended to explicitly specify any possible numbers following a continuous range (including its endpoints). Ordinal numbers or other qualifiers are used only as labels to distinguish elements with the same name, but do not in themselves indicate any priority, precedence, or order. Terms including, comprising, having, containing, relating to, etc., should be intended to have an open, non-exclusive meaning (i.e., not limited to the listed entries), terms based on, depending on, according to, functions of, etc., should be intended as non-exclusive relations (i.e., may involve other variables), the term one / a should be intended as one or more entries (unless otherwise explicitly indicated), and the term “apparatus for… (or any apparatus + functional formula)” should be intended as any structure suitable for or configured to perform the relevant function.

[0078] For example, one embodiment provides a method for imaging one or more body parts of a patient in a medical application. However, this method can be used for any medical application (e.g., surgery, diagnosis, treatment, laboratory analysis, etc.) to image any number and type of body parts (e.g., each representing one or more organs such as the liver, prostate, or heart; regions of organs such as those in healthy / illness conditions; quadrants such as the abdomen; tissues, etc.) of any patient (e.g., human, animal, obtained from a patient, obtained from a sample extracted from a patient, etc.). In any case, while this method may assist a physician's task, it provides intermediate results that can help him / her, but the medical activity in the strict sense is always performed by the physician himself / herself.

[0079] In this embodiment, the method includes the following steps under the control of a computing device. However, the computing device can be of any type (see below).

[0080] In an embodiment, the method includes providing (to a computing device) multiple luminescent images of body parts. However, the luminescent images can be acquired at any time (e.g., during one or more medical procedures, during one or more laboratory analyses, any combination thereof), and they can be provided in any manner (e.g., directly acquired, transmitted with removable storage, uploaded via a network, etc.); furthermore, the luminescent images can be associated with body parts in any way (e.g., representing body parts photographed from different imaging directions, different regions of a body part, different body parts, any combination thereof, etc.).

[0081] In embodiments, each of the luminescent images includes multiple values ​​representing the luminescent light emitted by a luminescent substance from a corresponding location on a body part. However, each luminescent image can have any size and shape (e.g., an entire frame or its region of interest (ROI)), and it can include values ​​of any type and location (e.g., grayscale or color values, for pixels or voxels, etc.). The luminescent light can be of any type (e.g., NIR, near-infrared (IR), visible light, etc.), and it can be emitted in any manner (e.g., in response to a corresponding excitation light or more generally in response to any other excitation other than heating) by any intrinsic / extrinsic or exogenous / endogenous luminescent substance (e.g., any luminescent agent, any naturally luminescent component, based on any luminescent phenomenon, such as fluorescence, phosphorescence, chemiluminescence, bioluminescence, induced Raman radiation, etc.).

[0082] In an embodiment, the method includes (by a computing device) determining a global range from the lowest to the highest value among all the values ​​of the luminescent images. However, the global range can be determined in any way (e.g., determined indiscriminately based on all values ​​of the luminescent images, filtered to remove outliers, considering only the luminescent images, or also considering comparison images, etc.).

[0083] In one embodiment, the method includes (by a computing device) determining a mapping function that maps a global range to the dynamic range of a display. However, the mapping function can be of any type (e.g., linear, logarithmic, etc.) and can be used for any display (e.g., monitor, virtual glasses, printer, etc.).

[0084] In an embodiment, the method includes (by a computing device) mapping each luminescent image to a corresponding mapped luminescent image by transforming the values ​​of the corresponding luminescent images according to a mapping function. However, the luminescent images can be mapped for any purpose (e.g., adjusting their values, logarithmic compression, saturation, etc.).

[0085] In an embodiment, the method includes (by a computing device) displaying a processed image, including a mapped luminescent image, together on a display. However, the processed images can be of any type (e.g., a mapped luminescent image alone, but also including (mapped) comparison images, segmented images, and / or overlay images), and they can be displayed in any manner (e.g., locally or remotely, with or without any descriptive labels, with or without any sorting indicators, etc.).

[0086] Other embodiments provide additional advantageous features; however, these advantageous features may be omitted entirely in the basic implementation.

[0087] Specifically, in an embodiment, the method includes providing (to a computing device) a light-emitting image acquired from a patient and / or a light-emitting image acquired from one or more samples taken from the patient. However, the light-emitting images can be acquired from the patient in any manner (e.g., from a living or deceased patient during one or more medical procedures of any type, such as acquiring one or more light-emitting images during a surgical / treatment procedure on a body part, acquiring one or more light-emitting images during a previous diagnostic procedure on the same body part, acquiring light-emitting images during different treatment procedures on the same body part, etc.), from any number and type of samples (e.g., excisional biopsy, core biopsy, etc.), or any combination thereof (e.g., by providing one or more light-emitting images acquired from the patient during any medical procedure on the body part together with one or more light-emitting images acquired from one or more samples taken from the patient of the same body part prior to performing the medical procedure).

[0088] In an embodiment, the method includes (by a computing device) determining a global range from the lowest to the highest value among all values ​​of the luminescent images to which outliers are filtered. However, the values ​​of the luminescent images can be filtered in any way (e.g., by discarding values ​​below and above any percentile, values ​​below and above any multiple of the quartile range, values ​​whose Z-score is below and above any threshold (such as ±3), etc.) at any time (e.g., by ignoring outliers during the determination of the global range, by pre-setting outliers in the luminescent images, etc.).

[0089] In an embodiment, the method includes providing (to a computing device) an luminescent image representing a common body part, different regions of the common body part, and / or different body parts from different imaging directions. However, the luminescent image may involve any number and type of imaging directions, regions, body parts, or any combination thereof (e.g., local, different, and additional imaging directions, regions, and body parts relative to the aforementioned imaging directions, regions, and body parts).

[0090] In one embodiment, the method includes prompting a user (by a computing device) to set up a corresponding imaging arrangement for the patient to continuously acquire luminescent images. However, the user may be prompted to set up any imagined arrangement for the patient (e.g., different imaging directions for common body parts, different regions of common body parts, different body parts, etc.) in any manner (e.g., by outputting corresponding visual messages, sound messages, etc.).

[0091] In an embodiment, the method includes (by a computing device) acquiring each of the luminescent images in response to user confirmation of the corresponding imaging arrangement. However, confirmation can be provided in any manner (e.g., by clicking a button, pressing a key, issuing a command, etc.); in any case, nothing prevents the free acquisition of luminescent images, and the corresponding descriptive labels may or may not be manually entered.

[0092] In an embodiment, the method includes receiving a manual adjustment of the dynamic range (by a computing device). However, the manual adjustment of the dynamic range can be provided in any manner (e.g., via any input unit such as a slider, up / down button, input box, etc., qualitatively or quantitatively, relatively or absolutely).

[0093] In an embodiment, the method includes repeating the determination of the mapping function, the mapped luminescent image, and the display of the processed image in response to manual adjustments of the dynamic range (by a computing device). However, these operations can be repeated in any manner (e.g., by replacing the mapped luminescent images with new versions of them, by displaying different versions of the mapped luminescent images together for comparison, etc.).

[0094] In one embodiment, the method includes (by a computing device) calculating a corresponding ranking index for a mapped luminescent image, the ranking index indicating the quality of the luminescent image corresponding to the mapping based on the content of the corresponding mapped luminescent image. However, the ranking index can be of any type (e.g., based on one or more central tendency static parameters, one or more dispersion statistics, any combination thereof, etc.).

[0095] In one embodiment, the method includes (by a computing device) displaying the processed image together with a corresponding ranking index of the mapped luminescent image on a display. However, the ranking index can be associated with the mapped luminescent image in any way (e.g., by displaying them close to the corresponding processed image in any way such as quantitative / qualitative, along with any visual indicator such as numbers, graphics, labels, colors, etc., or by simply ranking the processed image according to the ranking index of the processed image, etc.).

[0096] In one embodiment, the method includes (by a computing device) calculating a ranking index for each of the mapped luminescent images based on a central tendency statistical parameter of the values ​​of the mapped luminescent images. However, the central tendency statistical parameter can be of any type (e.g., mean, median, pattern, etc.).

[0097] In an embodiment, the method includes (by a computing device) providing at least one segmentation threshold, which is the same for a starting image equivalent to all luminescent images or mapped luminescent images, to separate the value of each in the starting image into a segmented number of fragments. However, the segmentation threshold can be any number and provided in any manner (e.g., automatically determined, manually set, etc.). In any case, the possibility of implementing mapping only (without any segmentation), segmentation only (without any mapping), segmentation followed by mapping, or mapping followed by segmentation (applied to the entire fluorescence image or applied to its individual fragments) is not excluded.

[0098] In an embodiment, the method includes (by a computing device) segmenting a corresponding starting image into the number of segments based on a comparison of the values ​​of the starting image with a segmentation threshold, and generating corresponding segmented images based on the starting images. However, each starting image can be segmented in any manner according to the segmentation threshold (e.g., binary, multi-level, multi-band, etc.).

[0099] In an embodiment, the method includes (by a computing device) displaying a further processed image based on the segmented image together with the processed image on a display. However, the processed image can be based on the segmented image in any way (e.g., including the segmented image, the segmented image superimposed on the corresponding reflective image, etc.). In any case, the possibility of using the segmented images in different ways is not excluded, even if they are not displayed (e.g., by calculating and outputting merged values ​​of different segments, any comparisons thereof, etc.).

[0100] In an embodiment, the method includes (by a computing device) segmenting each element of the starting image into detection segments and non-detection segments, respectively representing detected and undetected luminescent substances, based on a comparison of the values ​​of the starting image with a segmentation threshold. However, the corresponding processed image can be displayed in any manner using the detection / non-detection segments of each segmented image (e.g., the values ​​of non-detection segments are reset to black, the values ​​of detection segments are reset to color, and the values ​​of non-detection segments are reset to black and white, etc.). In any case, the possibility of segmenting the starting image in different ways (e.g., non-detection segments and multiple detection segments corresponding to different luminescent substances, etc.) is not excluded.

[0101] In an embodiment, the method includes (by a computing device) determining the at least one segmentation threshold based on the values ​​of all starting images to separate the values ​​of all starting images into the number of groups to be separated. However, the segmentation threshold can be determined in any manner (e.g., using techniques based on histogram shape, clustering, entropy, etc.).

[0102] In one embodiment, the method includes receiving a manual adjustment of the segmentation threshold (by a computing device). However, the manual adjustment of the segmentation threshold can be provided in any manner (whether the dynamic range is the same or different).

[0103] In an embodiment, the method includes repeating the segmentation starting image and the display processed image (by a computing device) in response to a manual adjustment of the segmentation threshold. However, these operations can be repeated in any manner (e.g., by replacing the segmented images with new versions of them, by displaying different versions of the segmented images together for comparison, etc.).

[0104] In an embodiment, the method includes (by a computing device) calculating a corresponding ranking index for a segmented image, the ranking index indicating the quality of the corresponding starting image for each segmentation based on the content of the segmented image. However, the ranking index can be of any type (e.g., any comparison based on one or more central tendency static parameters, one or more dispersion statistics, any combination thereof, etc.).

[0105] In one embodiment, the method includes (by a computing device) displaying the processed image together with a corresponding ranking index of the segmented image on a display. However, the ranking index can be associated with the segmented image in any way (e.g., the same or different relative to the mapped luminescent image).

[0106] In an embodiment, the method includes (by a computing device) calculating a ranking index for each segment of the segmented image based on comparisons between corresponding central tendency statistics of the values ​​of the segments of the segmented image. However, the ranking index can be any comparison (e.g., ratio, difference, etc.) based on any central tendency statistics (e.g., whether it is the same or different relative to the mapped luminescent image).

[0107] In one embodiment, the method includes providing (to a computing device) a plurality of reflected images corresponding to the emitted image. However, the reflected images can be provided in any manner (whether the reflected images are the same as or different from the emitted image).

[0108] In an embodiment, each of the reflected images includes multiple values ​​for the location of the body part, each value representing visible light reflected from the corresponding location. However, the reflected image can include any type of value (same or different relative to the luminescent image) for the same location of the body part, either directly or after appropriate magnification / reduction; furthermore, the values ​​of the reflected image can represent any visible light reflected by the body part (e.g., illumination by any light that does not cause noticeable luminescence).

[0109] In an embodiment, the method includes generating a plurality of overlay images (by a computing device) by superimposing segmented images onto corresponding reflective images. However, the segmented images can be overlaid onto the reflective images in any manner (e.g., in color and black and white, in different colors, etc.).

[0110] In one embodiment, the method includes (by a computing device) displaying a processed image, including an overlay image, on a display. However, the possibility of displaying a segmented image (as a supplement or alternative to the overlay image) is not excluded.

[0111] In an embodiment, the method includes retrieving one or more comparison images (by a computing device). However, the number of comparison images can be any, and they can be retrieved in any manner (e.g., read from mass storage, downloaded from a network, acquired locally, etc.).

[0112] In an embodiment, each of the comparison images includes multiple values ​​representing the emitted light by a luminescent substance from a corresponding location on the comparison entity. However, each comparison image may include any number of values ​​for any location (same or different relative to the fluorescence image) of any comparison entity (e.g., a reference device, other body parts of other patients, any combination thereof, etc.).

[0113] In an embodiment, the method includes (by a computing device) further determining a global range based on the values ​​of all comparison images. However, the global range can be determined in any way (e.g., by using retrieved comparison images or after any preprocessing of them, whether they are the same as or different from the luminescent image, etc.); in any case, the possibility of using separate global ranges for the luminescent image and the comparison images is not excluded.

[0114] In an embodiment, the method includes (by a computing device) mapping each comparison image to a corresponding mapped comparison image by transforming the values ​​of the comparison images according to a mapping function. However, the comparison images can be mapped in any way (e.g., the same or different relative to the luminescent image).

[0115] In an embodiment, the method includes (by a computing device) displaying a processed image, which also includes a mapped comparison image, on a display. However, the possibility of displaying the comparison image in its original version (as a supplement or replacement to the mapped comparison image) is not excluded.

[0116] In an embodiment, the method includes (by a computing device) retrieving a comparative image of at least one reference image comprising a reference device having one or more locations containing a luminescent substance of a corresponding known concentration. However, the reference images can be any number, each representing any number and type of location (e.g., any reference device having a fixed / removable container such as a vial or tube, filled with the same luminescent substance at different concentrations, and / or different luminescent substances, etc.).

[0117] In an embodiment, the method includes (by a computing device) retrieving comparative images, which include one or more evaluation images of one or more other body parts of other patients corresponding to the patient's body part. However, the evaluation luminescent images can be any number, each representing a body part of any patient (e.g., acquired with the same imaging system, acquired with different imaging systems, acquired at different locations, acquired under any healthy / pathological condition, etc.).

[0118] In this embodiment, the luminescent material is a luminescent agent pre-administered to the patient prior to the execution of the method. However, the luminescent agent can be of any type (e.g., any targeted luminescent agent, such as those based on specific or non-specific interactions, any non-targeted luminescent agent, etc.), and it can be pre-administered in any manner (e.g., using a syringe, infusion pump, etc.) at any time (e.g., pre-administered, immediately before the execution of the method, continuously during it, etc.). In any case, this is a data processing method that can be implemented independently of any interaction with the patient; furthermore, the luminescent agent can also be administered to the patient non-invasively (e.g., orally for gastrointestinal imaging, via a nebulizer into the airway, via local spray or local introduction during a surgical procedure, etc.) or in any situation where no substantial physical intervention (e.g., intramuscular) would require specialized medical expertise or pose any health risk to him / her.

[0119] In an embodiment, the luminescent material is a fluorescent material (the luminescent image is a fluorescent image and the value of each in the fluorescent image represents the fluorescence emitted by the fluorescent material at the corresponding location of the body part irradiated by the excitation light from the fluorescent material). However, the fluorescent material can be of any type (e.g., extrinsic or intrinsic, exogenous or endogenous, etc.) and responsive to any excitation light (e.g., NIR, IR, visible light, etc.).

[0120] The embodiments provide another method for imaging a patient in a medical application. In the embodiments, the method includes the following steps under the control of a computing device. In the embodiments, the method includes providing (to the computing device) multiple luminescent images of one or more body parts of the patient. In the embodiments, each of the luminescent images includes multiple values ​​representing luminescent light emitted by a luminescent substance from a corresponding location of the body part. In the embodiments, the method includes (by the computing device) determining at least one segmentation threshold based on the values ​​of all the luminescent images to separate the values ​​of all the luminescent images into groups of a number of segments. In the embodiments, the method includes (by the computing device) segmenting each of the luminescent images into said number of segments based on a comparison of the values ​​of the luminescent images with the segmentation threshold. However, each of these steps can be performed in any manner, and the optional features described above are added.

[0121] Generally, similar considerations apply if the same technical solution is achieved using equivalent methods (by using similar steps with more steps or a portion thereof that have the same function, removing some unnecessary steps, or adding other optional steps); furthermore, these steps can be performed in different orders, simultaneously, or in an interleaved manner (at least partially).

[0122] The embodiments provide a computer program configured to cause a computing device to perform the methods mentioned above when a computer program is executed on the computing device. The embodiments provide a computer program product comprising a computer-readable storage medium implementing the computer program, which can be loaded into the working memory of the computing device, thereby configuring the computing device to perform the methods. However, the (computer) program can be implemented as a standalone module, a plug-in to a pre-existing software program (e.g., an imaging system manager), or even directly within that plug-in. Similar considerations apply if the program is constructed differently, or if additional modules or functions are provided; similarly, the memory structure can be of other types or can be replaced by an equivalent entity (not necessarily composed of physical storage media). The program can take any form suitable for use by any computing device (see below), thereby configuring the computing device to perform desired operations; specifically, the program can be in the form of external or resident software, firmware, or microcode (in, for example, object code or source code to be compiled or translated). Furthermore, the program can be provided on any computer-readable storage medium. A storage medium is any tangible medium (unlike a transient signal itself) that can retain and store instructions for use by a computing device. For example, the storage medium can be of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor type; examples of such storage media are fixed disks (in which programs may be pre-loaded), removable disks, memory keys (e.g., USB type), etc. Programs can be downloaded from the storage medium or via a network (e.g., the Internet, a wide area network, and / or a local area network including transmission cables, fiber optics, wireless connections, network devices); one or more network adapters in the computing device receive the program from the network and forward it to one or more storage devices of the computing device for storage. In any case, the technical solutions according to embodiments of this disclosure are applicable even with hardware structures (e.g., electronic circuitry integrated into one or more chips of semiconductor material, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)) or with a combination of software and hardware that are appropriately programmed or otherwise configured.

[0123] The embodiments provide a computing device including means configured to perform the steps of the methods mentioned above. The embodiments also provide a computing device including circuitry for performing each step of the methods (i.e., any hardware appropriately configured, for example, by software). However, the computing device can be of any type (e.g., the central unit of an imaging system, a standalone computer, etc.).

[0124] The embodiment provides an imaging system including the computing devices mentioned above. However, the imaging system can be of any type (e.g., guided surgical equipment, endoscope, laparoscope, etc.).

[0125] In one embodiment, the imaging system includes an acquisition unit for acquiring an luminescent image. However, the acquisition unit can be of any type (e.g., based on any number and type of lenses, waveguides, mirrors, EMCCDs, CMOS, InGaAs, or PMT sensors, etc.).

[0126] In one embodiment, the imaging system includes an irradiation unit for applying excitation light to a body part. However, the irradiation unit can be of any type (e.g., based on a laser, LED, UV lamp, etc.).

[0127] In one embodiment, the irradiation unit is further used to apply white light to the body part. However, the irradiation unit can apply white light in any manner (e.g., with an LED, halogen / xenon lamp, etc.).

[0128] In this embodiment, the acquisition unit is further configured to acquire a reflected image. However, the reflected image can be acquired in any manner (e.g., with any number and type of lenses, waveguides, mirrors, CCDs, ICCDs, CMOS sensors, etc.).

[0129] Generally, similar considerations apply if the computing device and imaging system each have different structures or include equivalent components or have other operational characteristics. In any case, each component can be separated into more elements, or two or more components can be combined into a single element; furthermore, each component can be replicated to support the parallel execution of corresponding operations. Moreover, unless otherwise specified, any interaction between different components generally does not need to be sequential, and it can be direct or indirect through one or more intermediaries.

[0130] The embodiment provides a surgical method comprising the following steps: Imaging of a body part according to the method described above, thereby displaying the processed image on a monitor during the patient's surgical procedure. The patient is operated on based on the displayed processed image. However, the proposed method can be applied to any type of surgical procedure in the broadest sense of the term (e.g., for therapeutic purposes, for preventative purposes, for aesthetic purposes, etc.) and to any type of body part in any patient (see above).

[0131] The embodiment provides a diagnostic method comprising the following steps: Imaging of a body part according to the method described above, thereby displaying the processed image on a monitor during the patient's diagnostic process. The patient's health condition is assessed based on the displayed processed image. However, the proposed method can be applied to any type of diagnostic application in the broadest sense of the term (e.g., aimed at detecting new lesions, monitoring known lesions, etc.) and to analyzing any type of body part of any patient (see above).

[0132] The embodiment provides a treatment method comprising the following steps: Imaging of a body part according to the method described above, thereby displaying the processed image on a monitor during the patient's treatment. Treatment of the patient is performed based on the displayed processed image. However, the proposed method can be applied to any type of treatment method in the broadest sense of the term (e.g., aimed at curing a pathological condition, preventing its progression, preventing the occurrence of a pathological condition, or simply improving patient comfort) and acts on any body part of any patient (see above).

Claims

1. A method for imaging one or more body parts of a patient in a medical application, wherein, The method includes being controlled by a computing device: The computing device is provided with multiple emission images of body parts, each of which includes multiple values ​​representing emission light emitted by a luminescent substance from a corresponding location of the body part. The computing device scans the values ​​of all luminescent images to determine their minimum and maximum values, which respectively define the lower and upper limits of the global range of all luminescent images. The computing device determines a mapping function that maps the global range to the dynamic range of the display. The computing device maps each of the luminescent images to a corresponding mapped luminescent image by transforming the values ​​of the corresponding luminescent images according to the mapping function. The computing device displays the processed image, including the mapped luminescent image, on the display.

2. The method of claim 1, wherein, The method includes: Provide the computing device with luminescent images acquired from the patient and / or from one or more samples extracted from the patient.

3. The method of claim 1 or 2, wherein, The method includes: The computing device determines the minimum and maximum values ​​by filtering out outliers from all the values ​​of the luminous image.

4. The method of claim 1 or 2, wherein, The method includes: The computing device is provided with luminescent images representing a common body part, different regions of a common body part, and / or different body parts from different imaging directions.

5. The method of claim 1 or 2, wherein, The method includes: The computing device prompts the user to set the corresponding imaging arrangement for the patient to continuously acquire the luminescence images, and The computing device acquires each of the luminescent images in response to the user's confirmation of the corresponding imaging arrangement.

6. The method of claim 1 or 2, wherein, The method includes: The computing device receives manual adjustments to the dynamic range, and The computing device repeatedly responds to manual adjustments of the dynamic range to determine the mapping function, map the luminescent image, and display the processed image.

7. The method of claim 1 or 2, wherein, The method includes: The computing device calculates a ranking index corresponding to the mapped luminescent image, wherein each ranking index indicates the quality of the luminescent image corresponding to the mapping based on the content of the corresponding mapped luminescent image. The computing device displays the processed image and the corresponding sorting index of the mapped luminescent image together on the display.

8. The method of claim 7, wherein, The method includes: The computing device calculates a ranking index for each of the mapped luminescent images based on the central tendency statistical parameters of the values ​​of the mapped luminescent images.

9. The method according to claim 1 or 2, wherein, The method includes: The computing device provides at least one segmentation threshold that is the same for a starting image equivalent to all luminescent images or the mapped luminescent image, to separate the value of each in the starting image into a number of segments. The computing device segments the corresponding starting image into the specified number of segments based on a comparison between the value of the starting image and the segmentation threshold, thereby generating corresponding segmented images based on the starting image. The computing device then displays the further processed image based on the segmented image together on the display.

10. The method of claim 9, wherein, The method includes: The computing device divides each element in the starting image into detection segments and non-detection segments, representing the detection and non-detection of the luminescent material, respectively, based on a comparison between the value of the starting image and the segmentation threshold.

11. The method of claim 9, wherein, The method includes: The computing device determines the at least one segmentation threshold based on the values ​​of all starting images to separate the values ​​of all starting images into the specified number of segments.

12. The method of claim 9, wherein, The method includes: The computing device receives manual adjustments to the segmentation threshold, and The segmentation of the starting image and the display processed image is performed repeatedly by the computing device in response to manual adjustments of the segmentation threshold.

13. The method of claim 9, wherein, The method includes: The computing device calculates a ranking index for the segmented image, whereby each ranking index indicates the quality of the segmentation of the corresponding starting image based on the content of the segmented image. The computing device displays the processed image and the corresponding sorting index of the segmented image together on the display.

14. The method of claim 13, wherein, The method includes: The computing device calculates the ranking index for each segment in the segmented image based on a comparison between corresponding central tendency statistical parameters of the values ​​of the segments in the segmented image.

15. The method of claim 9, wherein, The method includes: The computing device is provided with a plurality of reflection images corresponding to the luminescent image, each of the reflection images including a plurality of values ​​representing visible light reflected from the corresponding location for the location of the body part. The computing device generates multiple superimposed images by overlaying the segmented images onto corresponding reflection images. The computing device displays the processed image, including the overlaid image, on the display.

16. The method of claim 1 or 2, wherein, The method includes: The computing device retrieves one or more comparison images, each of which includes multiple values ​​representing the emitted light from the luminescent material at a corresponding location of the comparison entity. The computing device further determines the global range based on the values ​​of all compared images. The computing device maps each of the comparison images to a corresponding mapped comparison image by transforming the values ​​of the comparison images according to the mapping function. The computing device displays the processed image, which also includes the compared image of the mapping, on the display.

17. The method of claim 16, wherein, The method includes: The computing device retrieves a comparative image including at least one reference image of a reference device having one or more locations containing luminescent material of corresponding known concentrations.

18. The method of claim 16, wherein, The method includes: retrieving comparative images by the computing device, the comparative images including one or more assessment images of one or more other body parts of other patients corresponding to the body part of the patient.

19. The method of claim 1 or 2, wherein, The luminescent substance is a luminescent agent that is pre-administered to the patient prior to performing the method.

20. The method of claim 1 or 2, wherein, The luminescent material is a fluorescent material, the luminescent image is a fluorescent image, and each value in the fluorescent image represents the fluorescence emitted by the fluorescent material at the corresponding location of the body part irradiated by the excitation light from the fluorescent material.

21. A computer program configured to cause a computing device to perform the method according to any one of claims 1 to 20 when the computer program is executed on the computing device.

22. A computer program product comprising a computer-readable storage medium for implementing a computer program, said computer program being loadable into the working memory of a computing device, thereby configuring said computing device to perform the method according to any one of claims 1 to 20.

23. A computing device comprising means configured to perform the steps of the method according to any one of claims 1 to 20.

24. A computing device comprising circuitry for performing each step of the method according to any one of claims 1 to 20.

25. An imaging system comprising a computing device according to claim 23 or 24 and an acquisition unit for acquiring the luminescent image.

26. The imaging system of claim 25, comprising the computing device configured to perform the steps of the method of claim 20, wherein, The imaging system includes an irradiation unit for applying the excitation light to the body part.

27. The imaging system of claim 26, comprising the computing device configured to perform the steps of the method of claim 15, wherein, The illumination unit is further configured to apply white light to the body part, the acquisition unit is further configured to acquire a plurality of reflection images corresponding to the luminescent image, each of the reflection images including a plurality of values ​​representing visible light reflected from the corresponding position for the position of the body part, and the computing device is further configured to generate a plurality of superimposed images by superimposing the segmented image on the corresponding reflection image, and to display the processed image including the superimposed image on the display.

Images