Segmentation verification of luminescence images limited to their analysis area

Abstract

A scheme is presented for imaging a field of view (103) comprising a target (115) containing luminescent substances. A corresponding method (500) comprises setting (512-552) an analysis region (225; 260) to a portion of a luminescent image (205; 205F) surrounding a suspicious representation of the target (115) and segmenting (554-556) the analysis region (225; 260) into a detection zone (230d; 265d) of luminescent substances and a non-detection zone (230n; 265n); then displaying (558-586) the luminescent image (205; 205F) with the detection zone (230d; 265d) highlighted according to a validation indicator based on a segmentation quality. A computer program (400) for implementing the method (500) and a corresponding computer program product are also presented. Moreover, a corresponding system (100) is presented. Further presented are a surgical method, a diagnostic method and a therapeutic method based on the same scheme.

Description

Technical Field

[0001] This disclosure relates to imaging applications. More specifically, this disclosure relates to luminescent imaging. Background Technology

[0002] The background of this disclosure is introduced below through a technical discussion in its context. However, even when this discussion relates to documents, actions, artifacts, etc., it does not imply or represent that the technology discussed is part of the prior art or common general knowledge in the field related to this disclosure.

[0003] Emission imaging, particularly fluorescence imaging, is a specific imaging technique used to acquire images that provide a visual representation of objects, even if they are not directly visible. This technique is based on a luminescence phenomenon, which involves luminescent materials emitting light when subjected to any excitation other than heating; specifically, fluorescence occurring in fluorescent materials (called fluorophores), which emit (fluorescent) light when illuminated. For this purpose, fluorescence images are typically displayed to represent fluorescence emitted from different locations within an object, followed by the fluorophores present therein.

[0004] Fluorescence imaging is commonly used in medical devices to examine (internal) body parts of a patient. In this context, a fluorescent agent (a specific molecule that may be suited to reach the desired target (e.g., a lesion such as a tumor) and then immobilized thereon) is typically applied to the patient. The representation of the (immobilized) fluorescent agent in the fluorescence image aids in the identification (and quantification) of the target. This information can be used in a variety of medical applications, such as identifying the edges of a lesion to be removed in surgical applications, detecting or monitoring lesions in diagnostic applications, and delineating lesions to be treated in therapeutic applications.

[0005] However, accurate identification of lesions remains quite challenging because it is adversely affected by several hindering factors. For example, lesions may emit varying amounts of fluorescence due to differences in the biology and perfusion of the lesion tissue. Furthermore, other parts of the field of view, different from the lesion, may emit interfering fluorescence that varies depending on their type; for example, interfering light may be caused by surgical instruments, hands, surgical tools, surrounding body parts (such as the skin around the surgical cavity or unrelated organs within it), and background material.

[0006] In particular, fluorescence images are often segmented into regions (regions) with essentially homogeneous features to distinguish fluorophores (and then the corresponding targets) from the rest of the field of view; specifically referring to medical applications, fluorescence image segmentation can be used to differentiate lesions from healthy tissue. For this purpose, the location of a body part is classified into a region of lesion or a region of healthy tissue by comparing the corresponding (fluorescence) value of each fluorescence image with a segmentation threshold. The segmentation threshold is typically calculated automatically based on the statistical distribution of fluorescence values. However, interfering light can skew the statistical distribution of fluorescence values, and thus deviate from the segmentation threshold (increasing or decreasing it). This involves the risk of misclassifying the location of the body part.

[0007] For example, in surgical applications, this can lead to uncertainty in the accurate identification of lesion margins (risk of incomplete lesion removal or excessive removal of healthy tissue). In diagnostic applications, this can adversely affect lesion identification and / or quantification, potentially leading to misinterpretations (risk of false positives / false negatives and incorrect follow-up). In therapeutic applications, this can adversely affect the depiction of lesions to be treated (risk of reduced treatment effectiveness or damage to healthy tissue).

[0008] US-A-2019 / 030371 discloses a technique for segmenting 3D medical images. For this purpose, a first neural network operating in 2D or 2.5D is used to generate a fast estimate of a small region of interest within a large 3D structure; then, a second neural network operating in 3D is used to obtain a more accurate segmentation of such regions of interest. Summary of the Invention

[0009] A simplified summary of the invention 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 the more detailed description that follows, and should not be construed as an identification of its key elements or a description of its scope.

[0010] Generally speaking, this disclosure is based on the idea of ​​verifying segmentation when the analysis region is limited.

[0011] In particular, one aspect provides a method for imaging a field of view including a target containing a luminescent substance. The method includes setting an analysis region as part of a suspicious representation of the luminescent image surrounding the target, and segmenting the analysis region into detection and non-detection partitions of the luminescent substance; then displaying the luminescent image, wherein the detection partitions are highlighted according to a verification metric based on segmentation quality.

[0012] On the other hand, a computer program for implementing the method is provided.

[0013] On the other hand, a corresponding computer program product is provided.

[0014] On the other hand, a system for implementing this method is provided.

[0015] On the other hand, a corresponding surgical method is provided.

[0016] On the other hand, it provides a corresponding diagnostic method.

[0017] On the other hand, it provides a corresponding treatment method.

[0018] More specifically, by means of the wording of all claims incorporated herein by reference verbatim, 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 (wherein any advantageous feature provided by reference to any particular aspect is applied to each of the other aspects with appropriate modifications). Attached Figure Description

[0019] The technical solutions disclosed herein, along with their further features and advantages, will be best understood by referring to the following specific embodiments, given only by non-limiting indication, and in conjunction with the accompanying drawings (wherein, for simplicity, corresponding elements are indicated by the same or similar reference numerals, their explanations are not repeated, and the name of each entity is generally used to indicate its type and attributes, such as value, content, and representation). In particular:

[0020] Figure 1 A schematic block diagram of an imaging system that can be used to implement the technical solutions according to embodiments of the present disclosure is shown.

[0021] Figures 2A to 2C and Figures 3A to 3D Different examples of the application of the technical solutions according to embodiments of this disclosure are shown.

[0022] Figure 4 The main software components that can be used to implement the technical solutions according to embodiments of this disclosure are shown, and

[0023] Figures 5A to 5C An activity diagram is shown that describes the flow of activities related to the implementation of the technical solutions according to embodiments of this disclosure. Detailed Implementation

[0024] Special reference Figure 1 The diagram shows a schematic block diagram of an imaging system 100 that can be used to implement the technical solutions according to embodiments of the present disclosure.

[0025] Imaging system 100 allows imaging of a scene within a corresponding field of view 103 (defined by imaging system 100 within a region of its sensitive solid angle). For example, imaging system 100 assists surgeons in surgical applications (commonly referred to as fluorescence-guided surgery (FGS), particularly fluorescence-guided resection (FGR) in tumor-related cases). In this specific case, field of view 103 relates to a patient 106 undergoing surgery, to whom a fluorescent agent (e.g., suitable for accumulation in a tumor) has been administered. Field of view 103 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. Field of view 103 may also include one or more foreign objects (not shown) other than surgical cavity 112, such as surgical instruments, hands, surgical tools, surrounding body parts, background material, etc. (surrounding or overlapping surgical cavity 112).

[0026] 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.

[0027] Starting with imaging probe 118, it has an illumination unit (for illuminating field of view 103) and an acquisition unit (for acquiring an image of field of view 103), which includes the following components. In the illumination unit, excitation light source 124 and white light source 127 generate excitation light and white light, respectively. The excitation light has a wavelength and energy suitable for exciting fluorophores of a phosphor (e.g., near-infrared or NIR type), while the white light is substantially colorless to the human eye (e.g., containing all wavelengths of the spectrum visible to the human eye at the same intensity). Corresponding transmission optics 130 and 133 transmit the excitation light and white light to the (same) field of view 103, respectively. In the acquisition unit, collection optics 136 collects the light from field of view 103 (in incident illumination geometry). The collected light includes fluorescence emitted by any fluorophores present in the field of view (illuminated by the excitation light). In practice, when a fluorophore absorbs 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, emitting fluorescence (at a characteristic wavelength longer than the excitation light wavelength, due to the energy dissipating as heat in the excited state), the intensity of which depends on the amount of fluorophore irradiated (and other factors, including the fluorophore's position within the field of view 103 and body part 109). Furthermore, the collected light includes reflected light from the visible spectrum, or visible light, which is reflected by any object present in the field of view (irradiated by white light). Beam splitter 139 splits the collected light into two channels. For example, beam splitter 139 is a dichroic mirror that transmits and reflects (or vice versa) collected light with wavelengths above and below the threshold wavelengths between the visible and fluorescence spectra, respectively.

[0028] In the (transmission) channel of beamsplitter 139, fluorescence is defined by a portion of the collected light in its spectrum. Emission filter 142 filters the fluorescence to remove any excitation light (which may be reflected by field of view 103) and ambient light (which may be generated by intrinsic fluorescence). A fluorescence camera 145 (e.g., an EMCCD type) receives the fluorescence from emission filter 142 and produces a corresponding fluorescence (digital) image representing the distribution of fluorophores in field of view 103. In another (reflection) channel of beamsplitter 139, visible light is defined by a portion of the collected light in its spectrum. A reflection camera or camera 148 (e.g., a CCD type) receives the visible light and produces a corresponding reflected or photographic (digital) image representing what is visible to the human eye in field of view 103.

[0029] Turning to the central unit 121, which comprises several units interconnected therebetween via a bus structure 151. Specifically, one or more microprocessors (μPs) 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 microprocessors 154. The central unit 121 is provided with a mass storage device 163 (e.g., a solid-state drive or SSD) for storing programs and data. Furthermore, the central unit 121 includes multiple controllers 166 for peripheral devices or input / output (I / O) units. Specifically, controller 166 controls the excitation light source 124, white light source 127, fluorescence camera 145, and 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 images, a keyboard for inputting commands, a trackball for moving pointers on the monitor, a driver for reading / writing removable storage units (e.g., USB keys), and a network interface card (NIC) for connecting to a communication network (e.g., a LAN).

[0030] Now refer to Figures 2A to 2C and Figures 3A to 3D This illustrates different examples of the application of the technical solutions according to embodiments of this disclosure. In particular, Figures 2A to 2C Involves (basic) unassisted operation mode, Figures 3A to 3D (Advanced) auxiliary operation modes related to the imaging system.

[0031] from Figure 2A (Unassisted operation mode) Start, fluorescence image 205 of field of view 103 has been acquired. Fluorescence image 205 provides a representation of surgical cavity 112 (with tumor) and various foreign objects, including skin 210 surrounding surgical cavity 112, tissue retractor 215 and clamp 220.

[0032] Go to Figure 2B In the technical solution according to the embodiments of this disclosure, the analysis region 225 is configured as a portion surrounding the fluorescence image 205 representing the (suspected) tumor. Specifically, in this specific implementation, the analysis region 225 has a predetermined shape and size (a small square equal to a predetermined portion of the fluorescence image 205), and it is located at the center of the fluorescence image 205 (which can be adjusted to include representations of the tumor and some adjacent healthy tissue).

[0033] Go to Figure 2C The analysis region 225 is segmented into a detection region 230d and a non-detection region 230n based solely on the fluorescence values ​​of the fluorescence image 205 within the analysis region 225. Detection region 230d indicates the detection of fluorescent agents, while non-detection region 230n indicates the absence of fluorescent agents; that is, in this case, (suspected) tumors and healthy tissues define their respective backgrounds. For example, a segmentation threshold is calculated based on the statistical distribution of fluorescence values ​​in the analysis region 225; based on a comparison of fluorescence values ​​with the segmentation threshold, the corresponding position in the field of view 103 is assigned to either detection region 230d or non-detection region 230n. Validation metrics are then determined based on the quality metrics of the segmentation of the analysis region 225. For example, the average fluorescence value of detection zone 230d (tumor intensity (TI) equals 6123 for the tumor in the figure) and the average fluorescence value of non-detection zone 230n (background intensity (BI) equals 4386 for the background of healthy tissue in the figure) are used; the validation metric is set as a quality index given by dividing the average of detection zone 230d by the average of non-detection zone 230n (tumor-to-background ratio (TBR) equals 1.4 in the figure). Fluorescence image 205 is then displayed, in which detection zone 230d is highlighted according to the validation metric. For example, the validation metric is compared to a validation threshold predefined for the type of surgery (TH = 1 in the figure); when the validation metric is higher than the validation threshold (1.4 > 1 in this case), detection zone 230d is colored; otherwise, it remains black and white (not shown in the figure).

[0034] The above-described technical solution significantly facilitates tumor identification. In fact, the limitation of segmenting only the (small) analysis region 225 allows for the exclusion of further fluorescence variations caused by interfering fluorescence from other parts of the tumor originating from the field of view 103.

[0035] In particular, the statistical distribution of fluorescence values ​​in analysis region 225 is now unbiased (because any interfering light affecting the rest of the fluorescence image 205 does not contribute to it). The result is a more precise segmentation threshold, thus reducing the risk of misclassifying the location of field of view 103.

[0036] Meanwhile, visualization of the fluorescence image 205 (representing the entire field of view 103) provides a representation of the tumor in the background of the body site; furthermore, this allows the analysis area 225 to be moved to other parts of the field of view 103 if necessary.

[0037] In any case, the (dynamic) highlighting of detection partition 230d according to validation metrics allows for identification only when a tumor should actually be present. In practice, when the segmentation quality is good enough, detection partition 230d is very likely to represent a tumor; in this case, the highlighted visualization of detection partition 230d is immediately apparent to the surgeon, allowing for the removal of that portion with high confidence associated with a true positive for the corresponding part of that body site. Conversely, when the segmentation quality is relatively poor, whether detection partition 230d truly represents a tumor is questionable; in this case, missing (or reduced) highlighting of the detection fragment will prevent the surgeon from operating on the corresponding portion of that body site due to the risk of false positives.

[0038] The above-mentioned technical solutions significantly reduce the risk of incomplete tumor removal or excessive removal of healthy tissue. All of these have beneficial effects on the patient's health.

[0039] Now refer to Figure 3A (In the auxiliary operating mode), a pair of corresponding reflectance images 205R and fluorescence images 205F have been obtained in the same field of view 103. The reflectance images 205R and fluorescence images 205F provide a representation of the coexistence of the surgical cavity 112 (with a tumor) and various foreign objects (in visible light and fluorescence, respectively), including skin 235 and tissue retractors 240 around the surgical cavity 112, as well as the surgeon's hand 245 that (partially overlaps) with the surgical cavity 112.

[0040] Go to Figure 3B In the technical solution according to an embodiment of this disclosure, an information region 250Ri is identified in the reflected image 205R based on its content. The information region 250Ri represents the surgical cavity without foreign objects (in this case, skin, tissue retractors, and the surgeon's hand), followed by the information portion (i.e., the region of interest or ROI) of actual surgical interest within the field of view 103. The remaining portion of the reflected image 205R then defines a non-information region 250Rn representing foreign objects, followed by non-information portions of the field of view 103 that are not of surgical interest. This result is achieved, for example, through semantic segmentation techniques (e.g., based on the use of neural networks), as described in detail below.

[0041] The identification of the information region 250Ri (and subsequently the non-information region 250Rn) in the reflectance image 205F is transferred to the fluorescence image 205F. Specifically, the (fluorescent) information region 250Fi corresponding to the information region 250Ri is identified in the fluorescence image 205F. As a result, the remainder of the fluorescence image 205F defines the (fluorescent) non-information region 250Fn corresponding to the non-information region 250Rn.

[0042] Go to Figure 3C Now, a similar analysis region 260 is set as part of an information region 250Fi (in fluorescence image 205F), which surrounds the representation of the (suspected) tumor. Specifically, in this particular implementation, the analysis region 260 has a predefined shape and size (a small square equal to a predefined portion of the information region 250Fi), and it is located at the center of the information region 250Fi (which can be adjusted to include representations of the tumor and some adjacent healthy tissue).

[0043] Go to Figure 3D Analysis region 260 is processed in the same manner as above. Therefore, analysis region 260 is segmented into detection region 265d and non-detection region 265n solely based on the fluorescence value of the fluorescence image 205F in analysis region 260 (by a segmentation threshold calculated based on the statistical distribution of its fluorescence values). A validation metric is determined based on the quality metric of the segmentation of analysis region 260 (e.g., set to the corresponding TBR). As shown in the figure, fluorescence image 205F is also displayed in this case, with detection region 265d highlighted according to the validation metric, for example, colored when the validation metric is above the validation threshold (otherwise remaining black and white, not shown in the figure).

[0044] The above implementation further improves the reliability of tumor identification. In fact, in this case, only the (informative) representation of the surgical cavity is considered, while the (non-informative) representation of foreign objects (around and / or overlapping with the surgical cavity) is automatically ignored. This avoids (or at least substantially reduces) any adverse effects of foreign objects on the segmentation of the analysis region 260. In particular, the statistical distribution of fluorescence values ​​on which the segmentation of the analysis region 260 is based is now unbiased (because fluorescence values ​​in the non-informative region 250Fn do not contribute to it).

[0045] Now refer to Figure 4 The main software components that can be used to implement the technical solutions according to embodiments of the present disclosure are shown.

[0046] All software components (programs and data) are collectively labeled with reference numeral 400. Software component 400 is typically stored in mass storage and, when the program is running, is loaded (at least partially) into the working memory of the central unit of the imaging system along with the operating system and other applications not directly related to the technical solutions of this invention (omitted in the figures for simplicity). The program is initially installed into the mass storage from, for example, a removable storage unit or from a communication network. In this respect, each program may be a module, fragment, or part of code, comprising one or more executable instructions for implementing a specified logical function.

[0047] Fluorescence manager 405 manages the fluorescence units of the imaging system (including an excitation light source and a fluorescence camera) dedicated to acquiring fluorescence images of a field of view suitable for illumination for this purpose. Fluorescence manager 405 (in write mode) accesses fluorescence image repository 410, which stores a sequence of fluorescence images acquired sequentially during ongoing imaging processing. Each fluorescence image is defined by a bitmap comprising a cell matrix (e.g., having 512 rows and 512 columns), each cell storing the (fluorescence) value of a pixel, which is a basic picture element corresponding to a (fluorescence) location in the field of view; each pixel value defines the brightness of the pixel as a function of the fluorescence intensity emitted from that location, and consequently, a function of the amount of fluorophore present therein (e.g., from black to white as the amount of fluorophore increases). Similarly, reflection manager 415 manages the reflection units of the imaging system (including a white light source and a camera) dedicated to acquiring reflected images of a field of view suitable for illumination for this purpose. The reflection manager 415 (in write mode) accesses the reflection image repository 420, which stores a sequence of reflection images acquired sequentially during the same imaging process (synchronously with the fluorescence images in the corresponding repository 410). Each reflection image is defined by a bitmap comprising a cell matrix (having the same or different size relative to the fluorescence image), each cell storing the (reflection) value of a pixel corresponding to a (reflection) location in the field of view (the same or different relative to the fluorescence location); each pixel value defines the visible light reflected from that location (e.g., its RGB components).

[0048] In an auxiliary implementation, the reducer 425 reduces the (selected) fluorescence image to its informational region by (semantically) segmenting the corresponding reflective image into its informational and non-informational regions (based on its content and possibly further based on the content of the fluorescence image), and then transferring the segmentation of the reflective image to the fluorescence image. The reducer 425 (in read mode) accesses the reflective image repository 420 and (optionally) the fluorescence image repository 410, and (in write mode) accesses the reduction mask repository 430. The reduction mask repository 430 stores (fluorescence) reduction masks that define the informational / non-informational regions of the fluorescence image. The reduction mask is formed by a cell matrix of the same size as the fluorescence image, with each cell storing a reduction flag indicating whether the corresponding pixel is classified as an informational or non-informational region; for example, the reduction flag is set (e.g., at logic 1) when the pixel belongs to an informational region, and reset (e.g., at logic 0) when the pixel belongs to a non-informational region. The reducer 425 also uses a variable (not shown) that stores a (reflective) reduction mask that defines the information / non-informative region of the reflective image in a similar manner (i.e., through a cell matrix of the same size as the reflective image, each cell storing a reduction flag indicating whether the corresponding pixel is classified as an informational or non-informative region). In any case, the setter 435 is used to set the analysis region (within the entire fluorescence image in an unassisted implementation or within its informational region in an assisted implementation). The setter 435 (in read mode) accesses the fluorescence image repository 410 and (possibly) the reduction mask repository 430. Furthermore, the setter 435 displays a user interface for manually adjusting the analysis region. The setter 435 (in write mode) accesses the analysis mask repository 440. The analysis mask repository 440 stores analysis masks that define the (common) analysis regions of the fluorescence image. The analysis mask consists of a cell matrix of the same size as the fluorescence image, each cell storing an analysis flag that is set (e.g., at a logic value of 1) when a pixel belongs to the analysis region, and reset (e.g., at a logic value of 0) otherwise. Segmenter 445 uses a thresholding technique to segment the analysis region of a fluorescence image into its detection and non-detection partitions. Segmenter 445 (in read mode) accesses the fluorescence image repository 410, the analysis mask repository 440, and (possibly) the reduction mask repository 430, and (in write mode) accesses the segmentation mask repository 450. Segmentation mask repository 450 includes an entry for each fluorescence image in repository 410; this entry stores a segmentation mask defining the detection / non-detection partitions of the analysis region of the fluorescence image.The segmentation mask consists of a matrix of cells the same size as the fluorescence image. Each cell stores a segmentation marker, which is set (e.g., at a logic value of 1) when a pixel belongs to a detection partition of the analysis region, and reset (e.g., at a logic value of 0) otherwise. That is, it is reset when a pixel belongs to a non-detection partition of the analysis region or is outside the analysis region, or possibly outside the information region. Verifier 455 verifies the segmentation of the fluorescence image (by calculating corresponding verification metrics and possibly comparing them to verification thresholds). Verifier 455 (in readout mode) accesses the fluorescence image repository 410, the segmentation mask repository 450, the analysis mask repository 440, and (possibly) the reduction mask repository 430. Furthermore, verifier 455 can also (in readout mode) access the verification threshold repository 460. The verification threshold repository 460 stores one or more verification thresholds corresponding to a target (e.g., different types of tumors in different organs). These verification thresholds have been predetermined based on clinical evidence and / or studies. For example, for each target, the same procedures described above are applied during several surgical procedures, and the corresponding validation metrics (for its segmentation) are recorded; the next pathology report of the corresponding biopsy determines whether the body part of the detected region actually represents the target (positive) or not (negative). A validation threshold for the target is calculated to optimize its identification accuracy; this identification is given by validation metrics higher than the validation threshold for positive pathology reports and lower than the validation threshold for negative pathology reports (e.g., thereby minimizing false negatives where the validation metrics are lower than the validation threshold for positive pathology reports). The validator 455 also displays a user interface for manually selecting the target type for each imaging procedure. The validator 455 (in write mode, and possibly read mode) accesses a validation result repository 465. The validation result repository 465 includes an entry for each fluorescence image in the corresponding repository 410. This entry stores the verification results of the segmentation of the fluorescence images; for example, the verification results are given by a verification index and / or a verification flag, which is set (e.g., at a logic value of 1) when the verification is positive (verification index is above the verification threshold) and reset (e.g., at a logic value of 0) when the verification is negative (verification index is below the verification threshold), possibly with additional information already used for verification segmentation (described below). The visualizer 470 visualizes these fluorescence images by highlighting the detection zones of the fluorescence images on the monitor of the imaging system. The visualizer 470 (in readout mode) accesses the fluorescence image repository 410, the segmentation mask repository 450, and the verification result repository 465.

[0049] Now refer to Figures 5A to 5C The diagram illustrates an activity flow describing the activities related to the implementation of the technical solutions according to embodiments of the present disclosure.

[0050] Specifically, this activity diagram illustrates an exemplary process that can be used to image a patient during surgical procedures using method 500. In this respect, each block may correspond to one or more executable instructions for implementing a specified logical function on the central unit of the imaging system.

[0051] Prior to surgery, medical personnel (e.g., nurses) administer a fluorescent agent to the patient. The fluorescent agent (e.g., indocyanine green, methylene blue, etc.) is adapted to reach a specific (biological) target (e.g., the tumor to be removed) and remain substantially immobilized therein. This can be achieved by using non-targeted fluorescent agents (adapted to accumulate in the target without any specific interaction, e.g., through passive accumulation) or targeted fluorescent agents (adapted to bind to the target through a specific interaction, e.g., by incorporating a target-specific ligand into a formulation of the fluorescent agent, e.g., based on chemical binding properties and / or physical structure, vascular properties, metabolic properties, etc., suitable for interaction with different tissues). The fluorescent agent is administered intravenously to the patient in a pellet (using a syringe); as a result, the fluorescent agent circulates within the patient's vascular system until it reaches and binds to the tumor; while any remaining (unbound) fluorescent agent is cleared from the blood pool (according to the corresponding half-life). 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 away from other parts of the patient's body, surgery can begin. The operator then turns on the imaging system.

[0052] In response, the process begins by proceeding from black starting circle 502 to box 504. At this point, if necessary, the operator selects the type of surgical target (i.e., organ and tumor) in the verifier's user interface; the verifier then retrieves the verification threshold for the (selected) target from the corresponding repository. The operator then places the imaging probe near the area on the patient where the surgical cavity has been opened by the surgeon; the operator then inputs an activation command into the imaging system (e.g., using its keyboard). In response, at box 506, the fluorescence manager and reflection manager respectively turn on the excitation and white light sources for illuminating the field of view. Moving to box 508, the fluorescence manager and reflection manager simultaneously acquire (new) fluorescence images and (new) reflection images, respectively, and add them to the corresponding repositories; in this way, fluorescence and reflection images are essentially acquired simultaneously, and they provide different representations of the same field of view (in terms of fluorescence and visible light, respectively) that are spatially consistent (i.e., there is a predictable correlation between their pixels, up to perfect identity). The fluorescence / reflection images are continuously displayed on a monitor in real time so that the operator can correct the position of the imaging probe and / or the patient. These operations continue until the surgical cavity is correctly imaged, and in box 510, the operator inputs initialization commands into the imaging system (e.g., using its keyboard) to set the analysis area in the (current) fluorescence image, possibly utilizing the corresponding (current) reflectance image. Now, depending on the operating mode of the imaging system (e.g., manually set during its configuration, default-defined, or uniquely available), the active flow branches in box 512. Specifically, in unassisted (operational) mode, box 514 is executed, while in assisted (operational) mode, boxes 516 through 520 are executed; in both cases, the active flow merges again in box 522.

[0053] Now referencing box 514 (no auxiliary mode), the setter sets the background space (used to set the analysis area) to the entire fluorescence image (extracted from the corresponding storage). Processing then proceeds to box 522.

[0054] Alternatively, referring to box 516 (auxiliary mode), the reducer segments the fluorescence image based on semantic segmentation of the reflection image (extracted from the corresponding repository), as described in co-pending international application PCT / EP2021 / 054162 dated February 19, 2021. In short, the reducer may apply one or more filters to improve the quality of the reflection image; in particular, histogram equalization may be applied when the reflection image fits a region of identification information but is not particularly bright (i.e., the average of its pixel values ​​falls within a corresponding threshold). Additionally or alternatively, the reducer may shrink the reflection image to reduce computational complexity, group its pixels into substantially homogeneous groups (each group represented by a group value based on the corresponding pixel values) to simplify semantic segmentation, and apply motion compensation algorithms (to align the reflection image with the fluorescence image) and / or warp algorithms (to correct distortion of the reflection image relative to the fluorescence image). The reducer then semantically segments the (possibly pre-processed) reflection image by applying a classification algorithm or deep learning technique. In the case of classification algorithms, the reducer generates a corresponding feature map by applying filters, possibly extracting one or more features from the reflection image or fluorescence image; the reducer then computes a reflection reduction mask by applying a specific classification algorithm (e.g., a Conditional Random Field (CRF) algorithm) to the feature map. The reducer may optionally refine the reflection reduction mask thus obtained (e.g., by assigning any broken portions of non-informative regions completely surrounded by informative regions to informative regions and / or by removing isolated misclassified pixels). Alternatively, in the case of deep learning techniques, the reducer applies the reflection image (and possibly the fluorescence image) to a neural network (e.g., U-Net), which directly generates the reflection reduction mask. In box 518, the reducer segments the fluorescence image into its informative and non-informative regions by transferring the corresponding segmentation of the reflection image to the fluorescence image. For this purpose, the reducer may optionally adjust the reflection reduction mask to fit the fluorescence image (by scaling it down / up when they have different sizes). The reducer then sets the fluorescence reduction mask to be equal to (possibly adjusted) the reflection reduction mask and saves it to the appropriate repository. In box 520, the setter sets the background space (used to set the analysis area) to the information region of the fluorescence image defined by the (fluorescence) reduction mask. Processing then proceeds to box 522.

[0055] Referring now to box 522, the setter initializes the analysis region to have an initial shape (e.g., a square) and an initial size. The initial size is equal to a default portion of the background space size (e.g., 1-10%, preferably 2-8%, more preferably 3-7%, e.g., 5%); for example, the size of the analysis region is set such that the number of pixels contained is equal to a default portion of the number of pixels in the background space (given by all units of the fluorescence image in unassisted mode or by units of a reduction mask whose reduction flag is set in assisted mode). Now, depending on the (further) operating mode of the imaging system (e.g., manually set during its configuration, default-defined, or uniquely available), the active flow branches again in box 524. Specifically, in manual (operation) mode, box 526 is executed, while in automatic (operation) mode, boxes 528 through 544 are executed; in both cases, the active flow merges again in box 546.

[0056] Now, referring to box 526 (manual mode), the setter initializes the analysis region with an initial position, centered in the background space. For example, the working box is defined as the smallest rectangular area enclosing the information region of the fluorescence image (in no-assistance mode) or the fluorescence image (in assistance mode); the setter determines the center of the working box and then aligns the center of the analysis region with it. Processing then proceeds to box 546.

[0057] Alternatively, referring to box 528 (automatic mode), the analysis region is initialized to the initial position of the best candidate region selected from multiple candidate regions (as candidates for initializing the analysis region); the candidate region is defined by moving a window with the initial shape and initial size of the analysis region within the same working frame as described above (i.e., the smallest rectangular area enclosing the information region in the fluorescence image in unassisted mode or in assisted mode). For this purpose, initially, the (current) candidate region is considered at the beginning of the working frame (e.g., at its upper left corner). Then, a loop is entered at box 530, where the activity flow branches according to the selection criteria for the candidate region (e.g., manually set during its configuration, default-defined, or uniquely available). Specifically, boxes 532 to 534 are executed in the case of a selection criterion based on a quality index, while box 536 is executed in the case of a selection criterion based on an intensity index; in both cases, the activity flow is merged again at box 538. Now referring to box 532 (quality index), the setter commands the segmenter to segment the candidate region into its (candidate) detection partition and (candidate) non-detection partition (described below). In box 534, the setter instructs the verifier to calculate the (candidate) quality metric for the segmentation of the candidate regions (as described below). Alternatively, referring to box 536 (Intensity Metric), the setter calculates the intensity metric of the candidate regions based on the pixel values ​​of the candidate regions within the background space (i.e., all pixel values ​​in unassisted mode or only pixel values ​​belonging to the information region in assisted mode), for example, equal to their average. Now turning to box 538, the setter compares the quality / intensity metric of the candidate regions with a running value (initialized to null) consisting of the quality / intensity metric of the candidate regions temporarily selected as analysis regions. If the quality / intensity metric is (possibly strictly) higher than the running value, this means that the candidate region is superior to the (temporary) analysis region. Therefore, in box 540, the setter temporarily sets the analysis region as a candidate region and replaces the running value with its quality / intensity metric. This process then continues to box 542; if the quality / intensity metric is (possibly strictly) lower than the running value (meaning the temporary analysis region remains so), it also goes directly to the same point from box 538. At this point, the setter verifies whether the last candidate region has been processed; this occurs when a candidate region has reached the very end of the working frame, which is opposite to the beginning of the working frame (e.g., the bottom right corner in the example shown). If not, then in frame 544, the setter moves to the next candidate region by moving along the scan path of the working frame; for example, the candidate region moves horizontally (to the right) one or more pixels away from the beginning until it reaches the relative boundary of the working frame, then moves vertically (downward) one or more pixels within the working frame, then moves horizontally (to the left) one or more pixels in the opposite direction until it reaches the relative boundary of the working frame, then moves vertically (downward) one or more pixels within the working frame, and so on.The process then returns to box 530 to repeat the same operation on the next candidate region. Alternatively, the same operation can be performed simultaneously (at least partially) to reduce computation time. Conversely, once all candidate regions have been processed, the loop exits by descending to box 546; thus, at the loop's exit, the analysis region is automatically set as the optimal candidate region.

[0058] Referring now to box 546, the setter adds a representation of the analysis area, thus initialized, to the fluorescence images displayed sequentially on the monitor (e.g., using a colored (e.g., red) outline of the analysis area over a black-and-white fluorescence image). In box 548, the surgeon can instruct the operator to manually adjust the analysis area via the setter's user interface; for example, a turntable can be provided to change the size of the analysis area and / or four buttons can be provided to move the analysis area in the corresponding directions (left, right, up, and down). This allows for the inclusion of a suspicious tumor representation in the analysis area even under unusual conditions (e.g., when the tumor is very large and / or close to the boundaries of the surgical cavity). Additionally or alternatively, in box 550, the surgeon can instruct the operator to move the imaging probe, or he / she can directly move the surgical cavity (to obtain the opposite movement of the analysis area on the corresponding fluorescence / reflectance image). In most practical cases, this allows for the inclusion of a suspicious tumor representation in the analysis area without changing its size and position via the setter's user interface; this is particularly advantageous when only the surgical cavity is moved, as it allows the surgeon to adjust the analysis area themselves without any contact with the imaging system (and therefore without any sterilization concerns). Once the surgeon confirms that the analysis area has been correctly positioned, in box 552, the operator enters a confirmation command into the imaging system (e.g., using its keyboard); in response, the setter saves the analysis mask defining the thus determined analysis area to the appropriate repository.

[0059] From this point onward, the analysis region in each (new) acquired fluorescence image is segmented into its detection and non-detection regions. To this end, in box 554, the segmenter determines a segmentation threshold for the analysis region based on the statistical distribution of the pixel values ​​(potentially within the information region), for example, by applying the Otsu algorithm (in order to minimize the intra-class variance of the detection and non-detection regions). For this purpose, the segmenter considers pixel values ​​in the fluorescence image whose analysis flag is set in the analysis mask, and further, whose reduction flag is set in the reduction mask (retrieved from the corresponding repository) in auxiliary mode; thus, in auxiliary mode, the analysis region can be arranged across the contour of the information region (e.g., when a tumor is near it) without being adversely affected by the content of the non-information regions of the fluorescence image. In box 556, the segmenter generates a segmentation mask by comparing pixel values ​​(possibly within the information region) of the analysis region with a segmentation threshold; specifically, for each pixel whose analysis flag is set in its analysis mask and further, whose reduction flag is set in its reduction mask in auxiliary mode, the corresponding segmentation flag is set if the corresponding pixel value in the fluorescence image (possibly strictly) is higher than the segmentation threshold, otherwise it is reset, while for each other pixel, the corresponding segmentation flag is always reset. The segmenter then saves the segmentation mask thus obtained to the corresponding repository.

[0060] The activity flow then branches at box 558 based on the evaluation mode of the validation metric for the segmentation of the analysis region (e.g., manually selected at the start of the imaging process, default-qualified, or uniquely available). Specifically, boxes 560 through 564 are executed in dual (evaluation) mode, while box 566 is executed in single (evaluation) mode; in both cases, the activity flow merges again at box 568. Now referring to box 560 (dual mode), the quality metric is determined based on a comparison between the detected and undetected partitions. Specifically, the segmenter calculates (detection) statistics for the detected partitions, e.g., equal to the average (TI) of its pixel values; for this purpose, the validator considers the pixel values ​​of the fluorescence image whose detection markers are positioned in the segmentation mask (retrieved from the corresponding repository). Similarly, in box 562, the segmenter calculates the (undetected) statistical parameters for the non-detected partitions, e.g., also equal to the average (BI) of its pixel values; for this purpose, the validator considers pixel values ​​of the fluorescence image such that their detection flag is reset in the segmentation mask, while their analysis flag is set in the analysis mask, and further, in the auxiliary mode, their reduction flag is set in the reduction mask (retrieved from the corresponding repository). In box 564, the validator calculates a quality metric, e.g., their ratio (TBR), based on a comparison between the detection and non-detection statistical parameters. Processing then proceeds to box 568. As an alternative reference box 566 (single mode), the quality metric is determined only based on the detected partitions. Specifically, the segmenter sets the quality metric to a continuity metric for the detected partitions, e.g., equal to the maximum value of a quantity proportional to the number of discontinuities (possibly weighted according to their size) that are discontinuous with respect to the main part of the detected partition. Processing then proceeds to box 568.

[0061] Referring now to box 568, the activity flow branches according to the verification mode of the imaging system (e.g., manually selected at the start of the imaging process, default-qualified, or uniquely available). Specifically, in dynamic (verification) mode, each fluorescence image is verified individually; this is well-suited for situations where the field of view changes continuously (e.g., in surgical applications), but its application in any other situation is not excluded. In this case, at box 570, the verifier sets the verification metric of the fluorescence image to its quality metric. Conversely, in static (verification) mode, multiple fluorescence images are considered for verification; this is well-suited for situations where the field of view remains substantially the same over time (e.g., in diagnostic / therapeutic applications), but its application in any other situation is not excluded (e.g., by considering a small number of fluorescence images). In this case, at box 572, the verifier sets the verification metric of the fluorescence image to the average of the quality metrics of a set of fluorescence images, including the current one and one or more previous fluorescence images (retrieved from a corresponding repository), for example, in a pre-qualified number, such as 5 to 10. This improves the reliability of the verification thanks to the corresponding smoothing of transient changes in the field of view. In both cases, in box 574, the validator can also compare the validation metric with the validation threshold (retrieved above for the target). The validator then stores the validation results thus obtained in the appropriate repository; specifically, the validation results include the validation metric and / or validation flags, which are set when the validation metric (possibly strictly) is above the validation threshold (positive validation) and reset otherwise (negative validation), possibly with additional information, such as statistical detection parameters, statistical non-detection parameters, and the validation threshold.

[0062] The activity flow then branches at box 576 based on the highlighting mode of the detection partition (e.g., manually selected at the start of the imaging process, default-limited, or uniquely available). Specifically, in selective (highlighting) mode, the activity flow further branches at box 578 based on the verification result (retrieved from the corresponding repository). If the verification result is positive (verification flag is set), then at box 580, the visualizer verifies whether the selection mode is independent. If so, then at box 582, the visualizer updates the fluorescence image by highlighting the pixels of the detection partition indicated in the segmentation mask (retrieved from the corresponding repository); for example, across the entire display area of ​​the monitor, the pixels of the detection partition are colored (e.g., red), and the brightness increases with the corresponding pixel value (while the rest of the fluorescence image is black and white). Returning to reference box 576, in progressive (highlighting) mode, processing descends to box 584; the same point is also accessible from box 580 when selective and progressive modes are combined. In both cases, the visualizer updates the fluorescence image by highlighting pixels of the detection zones indicated in the segmentation mask (retrieved from the corresponding repository) according to the highlighting intensity, which depends on the verification metric; for example, the pixels of the detection zones are colored such that their brightness increases with the corresponding pixel value (across the entire display area) and their wavelength increases with the verification metric (e.g., from blue to red in a separate progressive mode, or from yellow to red in a combination of progressive and selection modes), while the rest of the fluorescence image is black and white, and vice versa, i.e., the pixels of the detection zones are colored such that their wavelength increases with the corresponding pixel value and their brightness increases with the verification metric. The process then continues from box 582 to box 586 or box 582. If the verification result is negative (verification flag reset), the same point is reached directly from box 578 in selection mode as well; in this case, the fluorescence image remains unchanged, for example, completely black and white (including its detection zones). The visualizer now displays a fluorescence image on the monitor, showing the analysis area and possibly highlighted detection zones (along with any additional information that may be needed for verification). In this way, the coloration of the detection zones and / or their higher intensity provide the surgeon with an immediate indication of their actual likelihood of representing a tumor to be removed (also thanks to the use of the monitor's entire display area).

[0063] For example, when a surgeon wishes to move to a different part of the surgical cavity, in box 588, the setter verifies whether the operator has entered a change command into the imaging system (e.g., using their keyboard) to change the analysis area. If so, the process returns to box 512 to set the (new) analysis area as described above. Conversely, in box 590, the segmenter verifies whether the operator has entered an end command into the imaging system (e.g., using their keyboard) to end the imaging process. If not, the process returns to box 554 to repeat the same operation for the next fluorescence image. Otherwise, the process ends at the concentric black / white stop circle 592 (after the fluorescence manager and reflection manager have respectively turned off the excitation and white light sources).

[0064] Variation Example

[0065] 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 other embodiments, are possible. In particular, different embodiments of this disclosure may even be practiced without the specific details (e.g., numerical values) set forth in the foregoing description to provide a more thorough understanding thereof; conversely, well-known features may have been omitted or simplified to avoid unnecessary detail obscuring the description. Furthermore, as a matter of general design choice, it is explicitly desired that specific elements and / or method steps described in connection with any embodiment of this disclosure may be incorporated into any other embodiment. Moreover, items presented in the same group, as well as different embodiments, examples, or alternatives, should not be construed as being factually equivalent to each other (but they are independent and autonomous entities). In any case, each numerical value should be read as being subject to modification according to applicable tolerances; in particular, unless otherwise stated, the terms “substantially,” “approximately,” “roughly,” etc., should be understood as within 10%, preferably within 5%, more preferably within 1%. Furthermore, the range of each numerical value should be intended to explicitly specify any possible number along the continuum within the range (including its endpoints). Ordinal numbers or other qualifiers are used only as labels to distinguish elements with the same name, but they do not in themselves imply any priority, precedence, or order. Terms including, containing, having, containing, relating to, etc., should have an open, non-exhaustive meaning (i.e., not limited to the listed items), terms based on, depending on, according to, used as, etc., should be intended to represent a non-exclusive relationship (i.e., potentially involving more variables), terms one / a should be intended to represent one or more items (unless otherwise explicitly stated), and terms used for means of… (or any means plus function format) should be intended to represent any structure suitable for or configured to perform the relevant function.

[0066] For example, one embodiment provides a method for imaging a field of view. However, this method can be used to image any field of view for any purpose (e.g., medical applications, forensic analysis, defect / crack inspection, etc.).

[0067] In this embodiment, the field of view includes the target. However, the target can be of any type (e.g., body part, fingerprint, mechanical part, etc.).

[0068] In this embodiment, the target comprises a luminescent material. However, the luminescent material can be of any extrinsic / intrinsic or exogenous / endogenous type (e.g., any luminescent agent, any naturally luminescent component, based on any luminescent phenomenon, such as fluorescence, phosphorescence, chemiluminescence, bioluminescence, induced Raman radiation, etc.).

[0069] 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).

[0070] In one embodiment, the method includes providing a luminescent image of the field of view (to a computing device). However, the luminescent image can be provided in any manner (e.g., directly acquired, transmitted via a removable storage unit, uploaded via a network, etc.).

[0071] In an embodiment, the luminescent image includes a plurality of luminescence values ​​representing luminescent light emitted by the luminescent material from a corresponding luminescent location in the field of view. However, the luminescent image can have any size and shape, and it can include any type of luminescence value and for any luminescent location (e.g., grayscale or color values ​​of pixels or voxels, expressed in RBG, YcBcr, HSL, CIE-L*a*b, Lab colors, etc.); the luminescent light can be of any type (e.g., NIR, 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).

[0072] In an embodiment, the method includes (via a computing device) setting an analysis region as part of a suspicious representation of the luminescent image surrounding the target. However, the analysis region can be set in any way (e.g., within the entire luminescent image or only within its information area, predefined or determined manually, semi-automatically, or fully automatically at runtime, etc.).

[0073] In an embodiment, the method includes (via a computing device) segmenting the analysis region into detection and non-detection regions, respectively representing detected and undetected luminescent agents, based solely on the luminescence value of the analysis region. However, the analysis region can be segmented in any manner (e.g., using thresholding algorithms, classification algorithms, machine learning techniques, defined by appropriate masks, or directly in the fluorescence image, etc.).

[0074] In an embodiment, the method includes determining a validation metric (via a computing device) based on a quality metric of the segmented analysis region. However, the validation metric can be determined in any manner based on any quality metric (e.g., based on a comparison of detected and undetected regions, based solely on detected regions, etc.).

[0075] In an embodiment, the method includes displaying a fluorescence image (via a computing device), wherein detection zones are highlighted according to verification metrics. However, the fluorescence image can be displayed in any manner (e.g., on any display unit, such as a monitor, virtual reality glasses, etc., printed, locally or remotely, in real-time or offline, alone, in combination with / overlay with a corresponding reflective image, etc.); furthermore, the detection zones can be highlighted in any manner according to verification metrics (e.g., in color relative to the rest of black and white, in high brightness relative to the rest of low brightness, etc.) (e.g., selectively highlighted or not highlighted, progressively highlighted according to the highlighting intensity based on the verification metrics, combinations thereof, with or without any further information, etc.).

[0076] Further embodiments provide additional advantageous features, however these features may be omitted entirely in the basic implementation.

[0077] Specifically, in an embodiment, the method includes (via a computing device) displaying a luminescent image, wherein detection zones are selectively highlighted based on a verification metric. However, the detection zones can be selectively highlighted in any manner (e.g., simply based on a comparison of the verification metric with a threshold, with hysteresis, etc.).

[0078] In one embodiment, the method includes (via a computing device) displaying a luminescent image, wherein detection zones are highlighted or de-highlighted based on a comparison of a verification metric with a verification threshold. However, the verification threshold can have any value (e.g., a value that varies with the target, a fixed value, etc.).

[0079] In one embodiment, the method includes (via a computing device) displaying an luminescent image in black and white, wherein detection zones are highlighted or not highlighted, respectively, by displaying in color or black and white. However, detection zones can be displayed in color in any manner (e.g., displayed in a single color with brightness varying with their luminescence value, displayed in different colors according to their luminescence value, displayed in a single color with fixed brightness, etc.).

[0080] In one embodiment, the method includes (via a computing device) displaying a luminescent image, wherein detection zones are progressively highlighted according to a highlighting intensity based on a verification metric. However, the highlighting intensity can be of any type (e.g., different colors, brightness, etc.) and can depend on the verification metric in any way (e.g., according to any linear or nonlinear function, such as proportionally, exponentially, logarithmically, continuously or discretely, always or only when the detection zones must be highlighted as described above, etc.).

[0081] In one embodiment, the method includes displaying a luminescent image (via a computing device), wherein the detection zone is highlighted with a highlighting intensity proportional to the verification metric. However, this result can be achieved in any manner (e.g., the highlighting intensity is proportional to the entire verification metric or only to the portion exceeding the verification threshold, etc.).

[0082] In one embodiment, the method includes (via a computing device) displaying an luminescent image in black and white, wherein the detection zone is highlighted according to a verification metric by displaying it in a color with visual cues. However, this result can be achieved in any manner (e.g., alone or in combination with another visual cue depending on the luminescence value, such as a single color or different colors depending on the luminescence value and brightness depending on the verification metric, fixed brightness or brightness depending on the luminescence value and different colors depending on the verification metric, etc.).

[0083] In an embodiment, the method includes (via a computing device) determining a quality metric based on a comparison between the content of a detected partition and the content of a non-detected partition. However, this result can be achieved in any manner (e.g., by calculating the quality metric as a similarity index, inter-class variance, by aggregating the luminescence values ​​of the previous two partitions, or by direct aggregation, etc.).

[0084] In an embodiment, the method includes (using a computing device) calculating a detected value based on the luminescence value of a detected zone and a non-detected value based on the luminescence value of a non-detected zone. However, the detected / non-detected values ​​can be of any type (e.g., corresponding statistical parameters, such as mean, median, mode, variance, maximum / minimum, etc.).

[0085] In one embodiment, the method includes (via a computing device) calculating a quality index based on a comparison between detected and non-detected values. However, the quality index can be calculated from the detected and non-detected values ​​in any way (e.g., as a ratio, difference, etc. between them).

[0086] In an embodiment, the method includes (via a computing device) determining a quality metric solely based on the content of the detection partition. However, the quality metric can be determined in any way based on the content of the detection partition (e.g., by setting the quality metric to any statistical parameter of its luminescence values, such as their mean, median, mode, variance, or by setting the quality metric based on one or more qualitative attributes of the detection partition, such as its continuity, shape, regular / irregular boundaries, etc.).

[0087] In an embodiment, the method includes (via a computing device) calculating a quality index based on the continuity of the detection partition. However, this continuity can be defined in any way (e.g., based on the number of breaks in the detection partition, by weighting the breaks according to their size and / or distance, etc.).

[0088] In an embodiment, the method includes (via a computing device) initializing an analysis region as a portion of a background space equal to the luminescent image. However, the background space may be precisely set to the luminescent image, or more generally it may correspond to the luminescent image (e.g., equal to its predefined internal portion), and the analysis region may be initialized in any way based on the luminescent image (e.g., a location therein predefined or determined at runtime, with or without the possibility of updating it later, requiring or not requiring manual confirmation, etc.).

[0089] In an embodiment, the field of view includes a region of interest for imaging and one or more foreign objects distinct from that region of interest. However, the region of interest can be of any type (e.g., a surgical cavity, the cavity of an endoscopic procedure, an open type accessed via a cavity, or a closed type accessed via an incision, etc.), and the foreign objects can be of any number, any type (e.g., instruments, hands, tools, body parts, background material, etc.) and are arranged in any position (e.g., overlapping the region of interest to any extent, surrounding it, separating it, any combination thereof, etc.).

[0090] In one embodiment, the method includes providing an auxiliary image of the field of view (to a computing device). However, this auxiliary image can be provided in any manner (either the same as or different from the emitted image).

[0091] In an embodiment, the auxiliary image includes a plurality of auxiliary values ​​representing auxiliary light (different from the emitted light) received from a corresponding auxiliary position in the field of view. However, the auxiliary image can have any size and shape, and it can include any type of auxiliary values ​​(the same or different from the emitted image) for any auxiliary position; the auxiliary light can be any type of emitted light different from the emitted image (e.g., visible light, IR light, ultraviolet light (UV), other different wavelengths of emitted light, etc.).

[0092] In an embodiment, the method includes identifying auxiliary information regions of the auxiliary image based on the content of the auxiliary image (via a computing device), which represent regions of interest free of foreign objects. However, the auxiliary information region can be of any type (e.g., a single region, one or more non-overlapping regions, defined by a corresponding mask, or directly in the auxiliary image, etc.) and can be identified in any manner (e.g., by semantically / non-semantically segmenting the auxiliary image into auxiliary information regions and auxiliary non-information regions, by searching for auxiliary information regions in the auxiliary image, etc.).

[0093] In an embodiment, the method includes (via a computing device) identifying luminous information regions in a luminous image that correspond to auxiliary information regions. However, luminous information regions can be identified in any manner (e.g., by directly transmitting or via any adjustment of the identifier of the auxiliary information region, by any post-processing up to no post-processing, etc.). Furthermore, this operation can be performed indiscriminately, or it can be conditional on the quality of the identification of the auxiliary information regions; for example, a quality metric (for the entire process or its steps) can be calculated, and if the quality metric does not reach a corresponding threshold, all luminous locations are assigned to luminous information regions.

[0094] In an embodiment, the method includes (via a computing device) initializing an analysis region as a portion of a background space defined by a region of luminescent information. However, the background space can be defined in any way based on the region of luminescent information (e.g., equal to it, equal to the largest rectangular region of the fluorescence image it encloses, equal to the smallest rectangular region of the fluorescence image that encloses it, etc.), and the analysis region can be initialized in any way based on the region of luminescent information (whether the analysis region is the same as or different from the background space corresponding to the luminescent image).

[0095] In this embodiment, the auxiliary image is a reflected image, the auxiliary light is visible light, and the auxiliary value represents the visible light reflected from the corresponding auxiliary position in the field of view illuminated by white light. However, the white light (and the corresponding visible light) can be of any type (e.g., any non-luminescent light that does not cause significant luminescence to the luminescent material).

[0096] In an embodiment, the step of identifying the auxiliary information region includes semantically segmenting the auxiliary image (by a computing device). However, the auxiliary image can be semantically segmented in any way (e.g., using any type of classification algorithm based on any number and type of features, using deep learning techniques based on any neural network, based solely on the auxiliary image, or further based on a fluorescence image, etc.).

[0097] In this embodiment, the auxiliary image is semantically segmented into auxiliary information regions corresponding to at least one category of the region of interest and auxiliary non-information regions corresponding to one or more categories of foreign objects. However, the number and type of the region of interest and foreign object categories can be any number and any type (e.g., a single region of interest category for the entire region of interest, a single foreign object category for all foreign objects, multiple region of interest categories for a corresponding portion or group of the region of interest, multiple foreign object categories for a corresponding type or group of foreign objects, etc.).

[0098] In an embodiment, the method includes (via a computing device) initializing the analysis region as a portion of a background space having a predetermined initial shape. However, the initial shape can be of any type (e.g., square, circle, rectangle, etc.); in any case, the possibility of manually selecting the initial shape at runtime is not excluded.

[0099] In an embodiment, the method includes (via a computing device) initializing the analysis region as a portion of the background space having a predetermined initial size. However, the initial size can be of any type (e.g., a predefined percentage of the background space, a predefined value, etc.); in any case, the possibility of manually selecting the initial size at runtime (absolutely or relatively) is not excluded.

[0100] In an embodiment, the method includes (via a computing device) initializing the analysis region as a portion of the background space with a predetermined initial position. However, the initial position can be of any type (e.g., at the center, at the edge, etc.); in any case, the possibility of manually selecting the initial position at runtime is not excluded.

[0101] In an embodiment, the method includes (via a computing device) setting an initial size of the analysis region based on at least one predetermined portion of the background space size. However, this portion can be of any type and any value (e.g., a single value for area, two or more values ​​for corresponding dimensions, such as width and height, etc.).

[0102] In one embodiment, the method includes (via a computing device) setting the initial position of the analysis region to the center of the background space. However, the analysis region can be centered in the background space in any way (e.g., to its geometric center, to its centroid, etc.).

[0103] In an embodiment, the method includes (via a computing device) initializing an analysis region as a best candidate region selected from a plurality of candidate regions in a background space, these candidate regions becoming candidates for initializing the analysis region based on their corresponding content. However, the candidate regions can be any number and any type (e.g., moving at any interval throughout the background space and having a predetermined shape / size, having a shape and / or size that varies throughout the background space, etc.); the best candidate region can be selected in any manner (e.g., based on a corresponding quality index, intensity index, a combination thereof, etc.).

[0104] In an embodiment, the method includes (via a computing device) segmenting each candidate region into candidate detection partitions and candidate non-detection partitions, representing the detection and non-detection of luminescent agents, solely based on the luminescence values ​​of the candidate regions. However, each candidate region can be segmented in any manner (the same or different relative to the analysis region).

[0105] In one embodiment, the method includes (via a computing device) calculating a corresponding candidate quality metric for the segmented candidate region. However, each candidate quality metric can be determined in any way (either the same as or different from the quality metric).

[0106] In an embodiment, the method includes (via a computing device) selecting an optimal candidate region with an optimal candidate quality metric. However, the optimal candidate quality metric can be determined in any way (e.g., the highest or lowest metric as quality increases or decreases, respectively).

[0107] In an embodiment, the method includes (via a computing device) calculating corresponding intensity indices for candidate regions, each intensity index indicating the intensity of emitted light emitted only from the location of the corresponding candidate region. However, each intensity index can be calculated in any manner (e.g., based on any statistical parameter of the corresponding emission values, such as their mean, median, mode, variance, etc.).

[0108] In an embodiment, the method includes (via a computing device) selecting an optimal candidate region with an optimal intensity index. However, the optimal intensity index can be determined in any way (e.g., the highest or lowest index as the concentration of the luminescent material increases or decreases, respectively).

[0109] In one embodiment, the method includes (via a computing device) displaying a luminescent image having a representation of the analysis region. However, the luminescent image can be displayed in any representation of the analysis region (e.g., by its outline of any color, using any line, etc.).

[0110] In one embodiment, the method includes receiving manual adjustments to the analysis area (via a computing device). However, the analysis area can be adjusted in any way (e.g., by using any input unit such as a keyboard, trackball, mouse, numeric keypad, etc. to change its shape, size, and / or position).

[0111] In one embodiment, the method includes receiving confirmation of the analysis area (via a computing device) after the content of the field of view has been manually moved. However, after any movement of the field of view and / or the imaging probe, confirmation can be provided via any input unit (e.g., keyboard, trackball, mouse, dedicated button, etc.).

[0112] In an embodiment, the method includes providing (to a computing device) one or more additional luminescent images of the field of view, each additional luminescent image including a corresponding additional luminescence value representing the luminous light emitted by the luminescent substance from a location within the field of view. However, the additional luminescent images can be any number (e.g., a fixed number or up to all available luminescent images, luminescent images before and / or after the luminescent images, etc.), and they can be of any type and provided in any manner (same or different relative to the luminescent images).

[0113] In an embodiment, the method includes segmenting each of the additional analysis regions (by a computing device) into additional detection zones and additional non-detection zones, respectively representing detected and undetected luminescent agents, based solely on additional luminescence values ​​of the corresponding additional analysis regions from additional luminescence images of the additional analysis regions. However, the additional analysis regions can be segmented in any manner (either the same or different from the analysis regions).

[0114] In one embodiment, the method includes (via a computing device) determining additional quality metrics corresponding to the segmentation of additional analysis regions. However, the additional quality metrics can be determined in any manner (whether the same as or different from the original quality metrics).

[0115] In an embodiment, the method includes (via a computing device) determining a validation metric based on additional quality metrics. However, the additional quality metrics can be used to determine the validation metric in any way (e.g., by calculating a global quality metric, such as any statistical parameter equal to the quality metric and the additional quality metrics, such as their mean, median, or mode, and then using it to determine the aforementioned validation metric; or by determining corresponding local validation metrics based on the aforementioned quality metric and the additional quality metrics, and then using them to determine the validation metric, for example, considering a validation result as positive when at least one predetermined percentage is positive, or up to all are positive, etc.).

[0116] In one embodiment, the method includes (via a computing device) determining a segmentation threshold based on the statistical distribution of luminescence values ​​in the analysis region. However, the segmentation threshold can be determined in any way (e.g., based on statistical distribution, entropy, clustering, or object properties, etc.).

[0117] In one embodiment, the method includes (via a computing device) segmenting the analysis region into detection and non-detection zones based on a comparison of the luminescence value of the analysis region with a segmentation threshold. However, locations can be assigned to detection / non-detection zones in any manner based on the segmentation threshold (e.g., assigned to detection zones when they are (possibly strictly) above or below the segmentation threshold, etc.).

[0118] In this embodiment, the method is used to image a patient's body parts in a medical application. However, the medical application can be of any type (e.g., surgical, diagnostic, therapeutic, etc.). In any case, while the method may facilitate the physician's task, it only provides intermediate results that may be helpful to him / her, and medical activities are always strictly performed by the physician himself / herself; furthermore, the body parts can be of any type (e.g., organs such as the liver, prostate, or heart, regions, tissues, etc.), under any conditions (e.g., in a living organism, in a cadaver, extracted from the body, such as a biopsy sample, etc.), and from any patient (e.g., human, animal, etc.).

[0119] In this embodiment, the target is defined by target conditions of the body part. However, target conditions can be of any type (e.g., any pathological tissue, such as tumors, inflammation, etc., healthy tissue, etc.).

[0120] In this embodiment, the luminescent agent is 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 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.) and at any time (e.g., in advance, just before the execution of the method, continuously during the method, 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 in a non-invasive manner (e.g., orally for gastrointestinal imaging, via a nebulizer into the airway, via topical spray application, or via external intervention during surgery, etc.), or in any situation where no substantial physical intervention is performed on the patient that requires specialized medical expertise or poses any health risk to him / her (e.g., intramuscular injection).

[0121] In this embodiment, the field of view includes a surgical cavity that exposes a body part during surgery. However, the surgical cavity can be of any type used for any surgical procedure (e.g., a wound in minimally invasive surgery, a body opening in standard surgery, etc.).

[0122] In this embodiment, the luminescent material is a fluorescent material (the luminescent image is a fluorescent image, and the luminescence value represents the fluorescence emitted by the fluorescent material from the corresponding luminescent position illuminated by its excitation light). However, the fluorescent material can be of any type (e.g., extrinsic or intrinsic, exogenous or endogenous, etc.).

[0123] Generally, similar considerations apply if the same technical solution is achieved using equivalent methods (by using similar steps with more or partial equivalent functionality, removing some unnecessary steps, or adding more optional steps); furthermore, these steps can be performed in different orders, simultaneously, or in an interleaved manner (at least partially).

[0124] An embodiment provides a computer program configured to cause the computing device to perform the methods described above when executed on the computing device. An embodiment provides 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 same methods. However, the computer program can be implemented as a standalone module, a plug-in for a pre-existing software program (e.g., an imaging system manager), or even directly within the latter. Similar considerations apply in any case if the computer program is constructed differently, or provides additional modules or functionality; similarly, the memory structure can be of other types, or can be replaced by an equivalent entity (not necessarily composed of a physical storage medium). The computer program can take any form suitable for use with any computing device (see below), thereby configuring the computing device to perform the desired operations; in particular, the computer program can be in the form of external or resident software, firmware, or microcode (in the form of, for example, object code or source code to be compiled or interpreted). Furthermore, the computer program can be provided on any computer-readable storage medium. This storage medium is any tangible medium (unlike transient signals themselves) that can retain and store instructions used by the computing device. For example, the storage medium can be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor; examples of such storage media are hard disks (in which programs can be pre-loaded), removable disks, storage keys (e.g., USB type), etc. Computer programs can be downloaded from the storage medium or via a network (e.g., the Internet, WAN, and / or LAN, including transmission cables, fiber optics, wireless connections, network devices, and network equipment); one or more network adapters in the computing device receive the computer 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 can be implemented in themselves, even by hardware structures (e.g., by electronic circuitry integrated into one or more semiconductor material chips, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits), or by a combination of appropriately programmed or otherwise configured software and hardware.

[0125] One embodiment provides a system including components configured to perform the steps of the method described above. Another embodiment provides a system including circuitry (i.e., any hardware appropriately configured by software) for performing each step of the same method. However, the system can be of any type (e.g., any computing device, such as the central unit of an imaging system, a standalone computer, etc.).

[0126] In this embodiment, the system is an imaging system. However, the imaging system can be of any type (e.g., guided surgical equipment, endoscope, laparoscope, etc.).

[0127] In one embodiment, the imaging system includes an illumination unit for applying excitation light and, possibly, white light, to the field of view. However, the illumination unit can be of any type (e.g., based on a laser, LED, UV / halogen / xenon lamp, etc.).

[0128] In this embodiment, the imaging system includes an acquisition unit for acquiring fluorescence images and possible auxiliary images. However, the acquisition unit can be of any type (e.g., based on any number and type of lenses, waveguides, mirrors, CCDs, ICCDs, EMCCDs, CMOS, InGaAs, or PMT sensors, etc.).

[0129] Similar considerations generally apply if the system has a different structure or includes equivalent components, or has other operational characteristics. In any case, each component can be separated into multiple 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 stated, any interaction between different components generally does not need to be sequential; it can be direct interaction or indirect interaction through one or more middleware.

[0130] One embodiment provides a surgical method comprising the following steps: Imaging a patient's body part according to the method described above, thereby displaying a luminescent image during surgery on the body part, wherein detection zones are highlighted according to verification indicators. Manipulation of the body part is performed based on the displayed luminescent image. However, the proposed method can be found in any type of surgical approach in the broadest sense of the term (e.g., for therapeutic purposes, for preventative purposes, for aesthetic purposes, etc.), and can be used to manipulate any type of body part of any patient (see above).

[0131] One embodiment provides a diagnostic method comprising the following steps: Imaging a patient's body part according to the method described above, thereby displaying a luminescent image of the body part during a diagnostic process, wherein detection zones are highlighted according to verification indicators. Analysis of the body part is performed based on the displayed luminescent image. However, the proposed method can be applied in any type of diagnostic application in the broadest sense of the term (e.g., aimed at assessing health, detecting new lesions, monitoring known lesions, etc.), and can be used to analyze any type of body part of any patient (see above).

[0132] One embodiment provides a treatment method comprising the following steps: Imaging a patient's body part according to the method described above, thereby displaying a luminescent image of the body part during treatment, wherein detection zones are highlighted according to verification indicators. Treatment of the body part is performed based on the displayed luminescent image. However, the proposed method can be found in any type of treatment method in the broadest sense of the term (e.g., aimed at curing a pathological condition, preventing its development, preventing the occurrence of a pathological condition, or simply improving patient comfort), and can be used to treat any type of body part of any patient (see above).

[0133] In the embodiments, the surgical method, diagnostic method, and / or treatment method each include administering a luminescent agent to the patient. However, the luminescent agent may be administered in any manner (see above) or this step may be omitted entirely (in cases where the luminescent agent is endogenous).

Claims

1. A method for imaging a field of view, the field of view including a target containing a luminescent substance, wherein, The method includes: The system provides a luminous image of the field of view to the computing device. This luminous image includes multiple luminous values ​​representing the light emitted by the luminescent material from corresponding locations within the field of view. The analysis area is set as part of a suspicious representation of the target in the luminescent image using a computing device. The computing device divides the analysis area into detection zones and non-detection zones based solely on the luminescence value of the analysis area, representing the detection of luminescent agents and the absence of luminescent agents, respectively. The verification metrics are determined by a computing device based on the quality metrics of the segmented analysis region, and The luminescent image is displayed via a computing device, where the detection zones are highlighted according to verification metrics.

2. The method according to claim 1, wherein, The method includes: The luminescent image is displayed via a computing device, where detection zones are selectively highlighted based on verification metrics.

3. The method according to claim 2, wherein, The method includes: The luminescent image is displayed by a computing device, in which the detection zones are highlighted or not highlighted based on a comparison of the verification index with the verification threshold.

4. The method according to claim 2 or 3, wherein, The method includes: The luminescent image is displayed in black and white by a computing device, wherein the detection zones are highlighted or not highlighted by displaying them in color or black and white respectively.

5. The method according to claim 1, wherein, The method includes: The luminescent image is displayed by a computing device, in which the detection zones are progressively highlighted according to the highlighting intensity based on the verification criteria.

6. The method according to claim 5, wherein, The method includes: The luminescent image is displayed by a computing device, in which the detection zones are highlighted with a highlight intensity proportional to the verification indicators.

7. The method according to claim 5 or 6, wherein, The method includes: The luminescent image is displayed in black and white by a computing device, where the detection zones are highlighted by displaying colors with visual cues according to verification indicators.

8. The method according to claim 1, wherein, The method includes: The quality index is determined by the computing device based on the comparison between the contents of the detection zone and the contents of the non-detection zone.

9. The method according to claim 8, wherein, The method includes: The computing device calculates the detection value based on the luminescence value of the detection zone, and calculates the non-detection value based on the luminescence value of the non-detection zone. The quality index is calculated by computing equipment based on the comparison between the detected value and the non-detected value.

10. The method according to claim 1, wherein, The method includes: The quality indicators are determined by the computing equipment based solely on the contents of the testing zones.

11. The method according to claim 10, wherein, The method includes: The quality indicators are calculated by the computing equipment based on the continuity of the detection zones.

12. The method according to claim 1, wherein, The method includes: The analysis region is initialized using a computing device as a portion of the background space equal to that of the luminescent image.

13. The method according to claim 1, wherein, The field of view includes a region of interest for imaging and one or more foreign objects different from the region of interest. The method includes: An auxiliary image of the field of view is provided to the computing device. This auxiliary image includes multiple auxiliary values ​​representing auxiliary light, which, unlike the emitted light, is received from corresponding auxiliary positions within the field of view. The computing device identifies auxiliary information regions in the auxiliary image based on its content. These auxiliary information regions represent areas of interest free of foreign objects. The computing device identifies the luminous information region in the luminous image that corresponds to the auxiliary information region, and The analysis area is initialized by a computing device as a portion of the background space defined by the luminescence information region.

14. The method according to claim 13, wherein, The auxiliary image is a reflected image, the auxiliary light is visible light, and the auxiliary value represents the visible light reflected from the corresponding auxiliary position in the field of view illuminated by white light.

15. The method according to claim 13 or 14, wherein, The steps for identifying auxiliary information regions include: The auxiliary image is semantically segmented by a computing device into auxiliary information regions corresponding to at least one category of the region of interest and auxiliary non-information regions corresponding to one or more categories of foreign objects.

16. The method according to claim 11, wherein, The method includes: The analysis region is initialized by the computing device as a portion of the background space with a predefined initial shape, initial size, and / or initial position.

17. The method according to claim 16, wherein, The method includes: The initial size of the analysis area is set by a computing device based on at least one predefined portion of the size of the background space.

18. The method according to claim 16 or 17, wherein, The method includes: The initial position of the analysis area is set to the center of the background space using a computing device.

19. The method according to claim 12, wherein, The method includes: The analysis region is initialized by a computing device as the best candidate region selected from multiple candidate regions in the background space, wherein the multiple candidate regions serve as candidates for initializing the analysis region based on their corresponding content.

20. The method according to claim 19, wherein, The method includes: The computing device divides each candidate region into candidate detection zones and candidate non-detection zones, representing the detection and non-detection of luminescent agents, based solely on the luminescence value of the candidate region. The corresponding candidate quality index of the segmented candidate region is calculated by the computing device, and The best candidate region with the best candidate quality index is selected using computing equipment.

21. The method according to claim 19, wherein, The method includes: The computing device calculates the corresponding intensity index for each candidate region. Each intensity index indicates the intensity of light emitted only from the location of the corresponding candidate region. The best candidate region with the best intensity index is selected using computing equipment.

22. The method according to claim 1, wherein, The method includes: The computer displays an image of the luminescence representing the area being analyzed, and Manual adjustments to the analysis area are received via a computing device.

23. The method according to claim 1, wherein, The method includes: After manually moving the content of the field of view, the analysis area is confirmed by the computing device.

24. The method according to claim 1, wherein, The method includes: One or more additional emission images of the field of view are provided to the computing device, each additional emission image including a corresponding additional emission value representing the emission light emitted by the luminescent substance from a position within the field of view. Using a computing device, each of the additional analysis regions is divided into additional detection zones and additional non-detection zones, representing the detection of luminescent agents and the absence of luminescent agents, based solely on the additional luminescence values ​​of the corresponding additional analysis regions in the additional luminescence images of the additional analysis regions. The computing device determines the corresponding additional quality indicators for the segmented additional analysis regions, and The verification indicators are further determined based on other quality indicators using computing equipment.

25. The method according to claim 1, wherein, The method includes: The segmentation threshold is determined by a computing device based on the statistical distribution of luminescence values ​​in the analysis area, and The analysis area is divided into detection and non-detection zones by a computing device based on a comparison between the luminescence value of the analysis area and the segmentation threshold.

26. The method according to claim 1, wherein, The method is used to image body parts of a patient in medical applications, where the target is defined by the target conditions of the body part.

27. The method according to claim 26, wherein, The luminescent agent is pre-administered to the patient before the procedure is performed.

28. The method according to claim 26 or 27, wherein, The field of view includes the surgical cavity that exposes body parts during surgery.

29. The method according to claim 1, wherein, The luminescent material is a fluorescent material, the luminescent image is a fluorescent image, and the luminescence value represents the fluorescence emitted by the fluorescent material from the corresponding luminescent position illuminated by its excitation light.

30. A computer program product comprising a computer-readable storage medium implementing a computer program that can be loaded into the working memory of a computing device, thereby configuring the computing device to perform the method according to any one of claims 1 to 29.

31. A system comprising means configured to perform the steps of the method according to any one of claims 1 to 29.

32. The system according to claim 31, wherein, The system is an imaging system configured to perform the steps of the method according to claim 29, the imaging system including an illumination unit for applying excitation light to a field of view and an acquisition unit for acquiring a fluorescence image.

Images