System for characterizing regions of interest in biological tissue

Abstract

A system (101) for characterizing a region of interest of a biological tissue, comprising: an aiming device (110), the aiming device (110) being configured to generate a reflection image of a base surface of the biological tissue; a microscopic analysis device (120), the microscopic analysis device (120) being configured to detect a light beam emitted by the biological tissue in response to illumination of the biological tissue with a detection pattern included in the base surface and to generate microscopic analysis information, and being configured to determine a tissue characterization parameter from the microscopic analysis information; a processing module (140), the processing module (140) being configured to position each of a plurality of base surface images relative to the surface image of the region of interest, and to generate a graphic element for a characterization parameter from at least one first characterization parameter determined for at least some of the plurality of base surface images; and a display module (150), the display module (150) being configured to display a tissue diagram including the graphic element, the diagram being positioned relative to the surface image of the region of interest.

Description

Technical Field

[0001] The present description relates to systems for characterizing regions of interest of biological tissue, and has particular application to characterizing regions of interest of skin in the context of excision. Background Art

[0002] In the case of, for example, excision of a skin tumor, it is important for the surgeon to characterize the extent of the tumor as best as possible in order to extract all of the tumor tissue while limiting the extraction of healthy tissue surrounding the tumor. The outline of the spatial extent of the tissue area to be extracted is often referred to as the "surgical margin" or "excision margin." The excision margin should also advantageously take into account the presence of deep skin lesions.

[0003] Generally speaking, optical methods for determining resection margins in surgery are known.

[0004] Thus, for example, the article by A.Alfonso-Garcia et al. [Reference 1] describes an imaging technique based on FLIm (fluorescence lifetime imaging) multiphoton fluorescence in neurosurgery, which makes it possible to identify in real time the brain regions affected by the tumor and the healthy regions during the surgical intervention in order to define the surgical margins. More specifically, the region of interest is photographed by a camera. The FLIm fluorescence analysis is performed in parallel by an optical fiber moved by the operator over the region of interest, which allows the transmission of the excitation beam and the collection of the fluorescence signal. The characteristic parameters of the FLIm fluorescence analysis (i.e., the color-coded fluorescence lifetime) are superimposed on the video image in order to present the operator with an augmented reality view of the region of interest.

[0005] However, this technique is difficult to apply to the excision of skin tumors due to the required accuracy in defining the excision margin, firstly because there is no depth resolution of the fluorescence signal in the above-mentioned technique, and secondly because the inevitable movement of the biological tissue to be analyzed leads to possible positioning errors between the surface image acquired by the camera and the representation of the characteristic parameters of the fluorescence.

[0006] In the published patent application WO2020087164 [reference 2], optical coherence tomography (OCT) images are used to define the resection margins of resected tissue, for example to determine whether a tumor has been completely extracted (particularly in breast-conserving surgical interventions). A convolutional neural network (CNN) is trained to identify in the OCT images the probability of the presence of an abnormality in the probed area. The OCT images can be three-dimensional, thereby also making it possible to consider deep tumor areas to determine the resection margins. An annotation image of the three-dimensional image thus acquired can be generated, in which the probability of an abnormality in the region of interest is indicated.

[0007] However, the method described in [reference 2] is suitable for ex vivo applications, but not for in vivo applications. This is because, firstly, it takes a long time to acquire all the images from which the analysis is performed (typically several minutes). This is also because, even if artificial intelligence tools are used to identify abnormalities in OCT images, these images are still difficult for doctors to interpret in the context of in vivo representations (during which doctors are accustomed to observing surface images).

[0008] The present description proposes a system for characterizing regions of interest of biological tissue, in particular enabling in vivo characterization, in particular of the skin, to easily guide resection, which allows both excellent precision and ease of interpretation for the physician. Summary of the invention

[0009] In this specification, the term "include" has the same meaning as "include" or "contain", is inclusive or open, and does not exclude other elements that are not described or shown. In addition, in this specification, the term "about" or "substantially" is synonymous (meaning the same) with a lower margin and / or upper margin of 10% (e.g., 5%) of the corresponding value.

[0010] According to a first aspect, the present description relates to a system for characterizing a region of interest of biological tissue, the system comprising:

[0011] - a sighting device, the sighting device comprising:

[0012] - a full-field lighting device configured to illuminate the biological group with a first spectral band

[0013] Weaving for lighting;

[0014] - a two-dimensional detector comprising a detection area;

[0015] - a full-field imaging device comprising a first optical axis and configured to optically conjugate the elemental surface of the biological tissue with the detection area of ​​the two-dimensional detector, the two-dimensional

[0016] The detector generates a reflected image of the elemental surface during operation;

[0017] - A microscopic analysis device, comprising:

[0018] - a microscope objective comprising a second optical axis mechanically integrated with said first optical axis;

[0019] - a lighting path, the lighting path being configured to include a first lighting

[0020] pattern and illuminating the biological tissue with a second spectral band;

[0021] a detection path comprising the microscope objective, the detection path being configured to detect, with a detection pattern included in the elemental surface, a response of the biological tissue to the

[0022] Light beams emitted for illumination of biological tissue and generating microscopic analysis information;

[0023] - a data processing unit comprising

[0024] - a first processing module configured to determine from said microscopic analysis information at least one first biological tissue characterization parameter at a given number of points of said detection pattern;

[0025] - a second processing module, the second processing module being configured to

[0026] - positioning each elementary surface image of a plurality of elementary surface images, which are successively acquired by moving the full-field imaging device of the aiming path, relative to the surface image of the region of interest, and

[0027] - generating at least one first image element for the at least one first characteristic parameter from the at least one first characteristic parameter determined for at least some of the plurality of elementary surface images;

[0028] - a display module configured to display a tissue map comprising at least said first map element, said map being positioned relative to said surface image of said region of interest.

[0029] In the present specification, the illumination pattern depends on the illumination path of the microscopic analysis path and may include an illumination point, an illumination line or an illumination surface (e.g. a rectangular surface generated by scanning of an illumination point or an illumination line). An illumination point is more precisely defined as a diffraction pattern generated by focusing a collimated light beam incident on the objective lens of a microscope passing through the microscopic analysis path. In the case of a full-field microscopic analysis path, the illumination pattern may also include an illumination surface that is not generated by scanning, such as a surface having a circular geometry.

[0030] The light beam emitted by the biological tissue in response to illumination of the sample may be a reflected light beam, a backscattered light beam, or a light beam generated by an emission process at another wavelength (eg, fluorescence, Raman scattering, etc.).

[0031] Furthermore, the elementary surface is defined by the field in the object space of the full-field imaging device of the sighting device (referred to in this specification as the "effective field"). For example, when the effective field is limited by the optics of the sighting device, the elementary surface is defined, for example, by a circle, or when the effective field is limited by the detection area of ​​the two-dimensional detector of the sighting device, the elementary surface is defined by a rectangle. In this specification, the image of the elementary surface generated by the sighting device is also referred to as a "sighting image".

[0032] The detection pattern is included in the elementary surface generated by the aiming device and in the illumination pattern or is of the same order of magnitude and depends on the detection path of the microscopic analysis device. The detection pattern can include detection points, detection lines or detection surfaces (e.g. a rectangular surface generated by scanning of a line or, in the case of a full-field microscopic analysis path, a surface optically conjugated to a detection area of ​​a detector). The detection points are defined in the object space by elementary areas optically conjugated to elementary detectors of the detection path of the microscopic analysis device.

[0033] Due to an original combination of features, the characterization system according to the first aspect enables localization of at least one first tissue characterization parameter relative to the surface image of the region of interest at a given number of points of the detection pattern with excellent accuracy, ie better than a few micrometers.

[0034] Then, a first graphic element for at least one first characterization parameter can be generated from the first tissue characterization parameter located at a given number of points of the surface image of the region of interest. The graphic element can directly correspond to the association of given visual features (color, opacity, thickness, etc.) of these points or some of these points. The graphic element can also be generated by the application of linear operations (interpolation, averaging, etc.) associated with given visual features (color, opacity, contour, contour sharpness, etc.), in particular in order to form a continuous surface. The graphic element can also be generated by a morphological mathematical operation (dilation, erosion, etc.) applied to a continuous surface and associated with a given visual feature (color, opacity, contour, contour sharpness, etc.).

[0035] According to one or more exemplary embodiments, the elementary surface images have partial overlap, and the surface image of the region of interest is generated by mosaicking the plurality of elementary surface images. Then, positioning of the elementary surface images in the surface image of the region of interest is automatically performed.

[0036] For example, when acquiring an image of the elementary surface, combining by mosaicking is performed, thereby enabling, for example, real-time display of the acquired surface image and a map of the at least one first identification parameter, thereby, for example, allowing a physician to make a decision during acquisition.

[0037] According to one or more exemplary embodiments, the at least one first graph element is generated as the acquiring proceeds.

[0038] In other exemplary embodiments, the combination by mosaicking is created after acquiring all elementary surface images (aiming images). Thus, less processing resources are used in real time.

[0039] According to one or more exemplary embodiments, the surface image of the region of interest is generated by an imaging system independent of the characterization system. For example, the surface image of the region of interest is a dermoscopic image acquired by a dermatoscope.

[0040] In these examples, the positioning of the image of the base surface relative to the surface image of the region of interest can be achieved using known techniques, such as techniques involving identifying points of interest ("feature detection") and then matching the points of interest ("feature matching"). Such techniques are described, for example, in the article by S. Li [reference 3]. Therefore, for positioning, mosaicking of the base surface images is no longer required.

[0041] In some exemplary embodiments, the positioning of each of the multiple basic surface images relative to the surface image of the region of interest may include pre-processing steps performed on the multiple images, such as: local enhancement or denoising filtering to mask or highlight structures of interest, applying a histogram matching or normalization algorithm to a group of the images, applying a mosaic algorithm to a group of the images to increase the level of overlap with the surface image of the region of interest.

[0042] For example, in some exemplary embodiments, the display module is configured to additionally display a surface image of the region of interest, which is easy for a doctor to interpret. In other exemplary embodiments, for example, when the surface image is obtained by mosaicking the plurality of basic surface images, the display module may be configured to additionally display another surface image of the entire region of interest, which is acquired by another imaging system (e.g., a dermoscopic image acquired by a dermatoscope) and is positioned relative to the surface image of the region of interest acquired by the characterization system according to the first aspect.

[0043] For example, in some exemplary embodiments, the display module is configured to display the tissue map in a manner superimposed on the surface image of the region of interest, or, for example, when the surface image is acquired by mosaicking the multiple basic surface images, to display the tissue map in a manner superimposed on another surface image of the entire region of interest, which is acquired by another imaging system and is positioned relative to the surface image of the region of interest acquired by the characterization system according to the first aspect.

[0044] In other exemplary embodiments, the display module may be configured to display the tissue map in juxtaposition with the surface image of the region of interest, or, for example, when the surface image is acquired by mosaicking the plurality of elementary surface images, to display the tissue map in juxtaposition with another surface image of the entire region of interest, the other surface image being acquired by another imaging system and being positioned relative to the surface image of the region of interest acquired by the characterization system according to the first aspect. In these exemplary embodiments, the display module may additionally be configured to show a common cursor in the tissue map and the surface image of the region of interest (that is, a graphical element configured to locate the same spatial position on the tissue map and the surface image of the region of interest).

[0045] Of course, the display module may be configured to operate in one and / or another of the above exemplary embodiments according to a user's selection.

[0046] In any case, the resection is facilitated because the physician can easily and precisely identify the contours of the region of interest (e.g. the contours of a tumor) at the level of the tissue surface, even if it cannot be directly seen on the surface. This precision is made possible in particular due to the mechanical linkage between the first optical axis of the full-field imaging device of the aiming device and the second optical axis of the microscope objective of the microscopic analysis device.

[0047] According to one or more exemplary embodiments, the full-field imaging device of the sighting device comprises the microscope objective. In these exemplary embodiments, the first optical axis and the second optical axis are coincident, thereby making it possible to have a more compact characterization system while benefiting from a high-quality sighting image obtained with the microscope objective. Furthermore, the positioning of the at least one first characterization parameter on the surface image is simplified.

[0048] In other exemplary embodiments, the full-field imaging device of the sighting device does not include a microscope objective of the microscopic analysis device. In this case, however, the mechanical linkage of the first optical axis and the second optical axis will enable the positioning of the detection pattern in the sighting image and thus the positioning of the at least one first parameter on the surface image of the region of interest.

[0049] In some embodiments, the first spectral band of the full-field illumination device of the aiming device and the second spectral band of the illumination path of the microscopic analysis device are at least partially different. In some exemplary embodiments, this makes it possible to at least partially filter the light coming from the illumination path of the microscopic analysis device and incident on the aiming device, or vice versa, to at least partially filter the light coming from the full-field illumination device of the aiming device and incident on the microscopic analysis system.

[0050] According to one or more exemplary embodiments, the full-field illumination device of the aiming path includes a plurality of light sources, which are arranged around the distal end face of the microscope objective (i.e., the face of the microscope objective in the space of the biological tissue). This configuration allows direct illumination of the biological tissue. Of course, other configurations can be used for the full-field illumination device of the aiming path.

[0051] According to one or more exemplary embodiments, the illumination path of the microscopic analysis device is configured to illuminate the biological tissue through the microscope objective. In other exemplary embodiments, the biological tissue may be directly illuminated, for example, through an optical fiber without involving a microscope objective.

[0052] According to one or more exemplary embodiments, the microscopic analysis path is a confocal imaging path and / or an optical coherence tomography imaging path, and the biological tissue microscopic analysis information includes at least one image of the biological tissue. For example, the microscopic analysis path is an optical coherence tomography imaging path as described in the prior art, and is configured to form a B-scan image or a C-scan image (or a frontal image) of the biological tissue or a 3D image of the biological tissue. In a known manner, a cross-sectional image of a biological tissue, referred to as a "B-scan", is an image formed in a plane parallel to the optical axis of a microscope objective; a sample cross-sectional image or a frontal image, referred to as a "C-scan", is an image formed on a plane perpendicular to the optical axis of a microscope objective, and a 3D image of a biological tissue is generated by acquiring multiple B-scan images or C-scan images, and thus allows analysis of biological tissue in a volume.

[0053] According to one or more exemplary embodiments, the microscopic analysis path is a spectral analysis path, and the biological tissue microscopic analysis information includes at least one spectrum of the light beam emitted by the sample at at least one point of the sample.

[0054] According to one or more exemplary embodiments, the at least one first biological tissue characterization parameter on which the first processing module generates a tissue map from the microscopic analysis information is based, is a parameter selected from the following: morphological measurements, cytological measurements, optical measurements, measurements characterizing chemical composition, mechanical measurements, combinations of these measurements, scores for characterizing tissue state (e.g., based on at least one of these measurements, for example, a score indicating the probability of the presence of a lesion (e.g., a lesion of a predetermined nature)).

[0055] According to one or more exemplary embodiments, the first module for processing the microscopic analysis information includes an artificial intelligence module, for example, based on a deep learning model using a neural network.

[0056] In general, the processing modules, processing units or control units mentioned in this specification may include one or more physical entities and may be combined in one or more computers. When referring to calculations or processing steps for implementing, in particular, method steps, in this specification, it should be understood that each calculation or processing step can be implemented by software, hardware, firmware, microcode or any suitable combination of these technologies. When software is used, each calculation or processing step can be implemented by computer program instructions or software code. These instructions can be stored or transferred to a storage medium that can be read by a control unit and / or executed by a control unit to implement these calculations or processing steps.

[0057] Therefore, in the characterization system according to the first aspect, the first processing module and the second processing module of the processing unit may be combined in one or more computers.

[0058] In some exemplary embodiments, the biological tissue is skin, and the characterization system is configured to be implemented in vivo. The characterization system enables, for example, the characterization of cancerous lesions.

[0059] According to one or more exemplary embodiments, the second processing module is further configured to determine a resection margin of the region of tissue to be extracted based on the at least one first image element.

[0060] For example, the resection margin is determined from the contour defined by the map by adding an additional margin, such as an additional margin of between about 0.5 mm and about 5 mm.

[0061] According to a second aspect, the present description relates to a computer program product comprising program code instructions for implementing a data processing method for characterizing a region of interest of biological tissue when said program is executed on a computer, said data being obtained by an optical analysis system comprising:

[0062] - a sighting device, the sighting device comprising:

[0063] - a full-field lighting device configured to illuminate the biological group with a first spectral band

[0064] Weaving for lighting;

[0065] - a two-dimensional detector comprising a detection area;

[0066] - a full-field imaging device comprising a first optical axis and configured to optically conjugate the elemental surface of the biological tissue with the detection area of ​​the two-dimensional detector, the two-dimensional

[0067] The detector generates a reflected image of the elemental surface during operation;

[0068] - A microscopic analysis device, comprising:

[0069] - a microscope objective comprising a second optical axis mechanically integrated with said first optical axis;

[0070] - a lighting path, the lighting path being configured to include a first lighting

[0071] pattern and illuminating the biological tissue with a second spectral band;

[0072] a detection path comprising said microscope objective, said detection path being configured to detect a light beam emitted by said biological tissue in response to illumination of said biological tissue with a detection pattern comprised in said elemental surface and to generate microscopic analysis information;

[0073] The data processing method comprises the following steps:

[0074] - determining from the microscopic analysis information the

[0075] at least one first biological tissue characterization parameter;

[0076] - positioning each elementary surface image of a plurality of elementary surface images, which are successively acquired by moving the full-field imaging device of the aiming path, relative to the surface image of the region of interest; and

[0077] - From the at least one first characteristic parameter determined for at least some of the plurality of elementary surface images, generating at least one first graphic element for the at least one first characteristic parameter.

[0078] The optical analysis system is, for example, a system as described in [Reference 7].

[0079] Thus, the computer program product may be executed on a computer which is not part of the optical analysis system.

[0080] According to one or more exemplary embodiments, the basic surface images have a partial overlap, and the surface image of the region of interest is generated by mosaicking the plurality of basic surface images.

[0081] According to one or more exemplary embodiments, the at least one first biological tissue characterization parameter is determined by an artificial intelligence module based on a deep learning model using a neural network.

[0082] According to one or more exemplary embodiments, the method further comprises determining a resection margin of the region of tissue to be extracted based on the at least one first image element.

[0083] According to a third aspect, the present description relates to a non-transitory computer-readable storage medium storing a computer program product according to the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other advantages and features of the present invention will become apparent from a reading of the specification, which is illustrated by the following drawings:

[0085] [ Figure 1A ] shows a diagram illustrating a first example of a system for characterizing a region of interest of biological tissue according to the present specification;

[0086] [ Figure 1B ] shows a diagram illustrating a second example of a system for characterizing a region of interest of biological tissue according to the present specification;

[0087] [ Figure 2 ] shows a diagram illustrating an example of a system for characterizing a region of interest of biological tissue, performing optical coherence tomography imaging microscopy analysis;

[0088] [ Figure 3A ] shows a diagram illustrating in simplified form the steps of a method for characterizing a region of interest of a biological tissue implemented by an example of a characterization system according to the present specification;

[0089] [ Figure 3B ] shows an example of display of a surface image of a region of interest obtained by an example of a method according to the present specification, the surface image of the region of interest being superimposed on a graph including a plurality of graph elements respectively for various characterization parameters;

[0090] [ Figure 3C] shows an example of display of a surface image of a region of interest obtained by an example of a method according to the present specification, the surface image of the region of interest being juxtaposed with a graph including a plurality of graph elements respectively for various characterization parameters;

[0091] [ Figure 4A ] shows a diagram showing a block diagram of a method according to the present description according to a first example;

[0092] [ Figure 4B ] shows a diagram showing a block diagram of a method according to the present description according to a second example;

[0093] [ Figure 5A ] shows an explanation as Figure 4A A diagram of the steps of the characterization method described in;

[0094] [ Figure 5B ] shows an explanation as Figure 4A A diagram of the steps of the characterization method described in;

[0095] [ Figure 5C ] shows an explanation as Figure 4A A diagram of the steps of the characterization method described in;

[0096] [ Figure 5D ] shows an explanation as Figure 4A A diagram of the steps of the characterization method described in;

[0097] [ Fig. 6A ] shows a diagram showing a block diagram of a method according to the present description according to a third example;

[0098] [ Figure 6B ] shows a diagram of a block diagram of a method according to the present specification according to a fourth example. DETAILED DESCRIPTION

[0099] In the following detailed description, a large number of specific details are set forth to provide a deeper understanding of this specification. However, it is apparent to those skilled in the art that this specification can be implemented without these specific details. In other cases, well-known features are not described in detail to avoid unnecessarily complicating the specification.

[0100] Furthermore, in the drawings, elements are not shown to scale for better visibility.

[0101] Figure 1A A diagram illustrating a first example 101 of a system for characterizing a region of interest of biological tissue S according to the present specification is shown, and Figure 1B A diagram illustrating a second example 102 of a system for characterizing a region of interest of biological tissue in accordance with the present specification is shown.

[0102] The characterization systems 101, 102 each include a sighting device 110, which includes a full-field illumination device 111 (e.g., a group of light-emitting diodes or LEDs) configured to illuminate the biological tissue with a first spectral band, a two-dimensional detector (not shown in the figure), and a full-field imaging device (not shown in the figure), and only the optical axis Δ1 of the full-field imaging device is schematically shown. The full-field imaging device of the sighting device is configured to make the basic surface of the biological tissue optically conjugate with the detection area of ​​the two-dimensional detector, and during operation, the two-dimensional detector generates a reflected image of the basic surface (referred to as a "sighting image" in this specification). For example, the sighting device 110 includes a mounting 115, which is configured to accommodate all or some of the optoelectronic components that form the full-field imaging device and the two-dimensional detector.

[0103] The characterization systems 101 , 102 each additionally comprise a microscopic analysis device 120 , which comprises a microscope objective 125 , which comprises an optical axis Δ2 , which is mechanically integrated with an optical axis Δ1 of the full-field imaging device.

[0104] exist Figure 1A In the example of the characterization system 101 shown, the optical axis Δ1 of the full-field imaging device of the sighting device 110 and the optical axis Δ2 of the microscope objective 125 are different. The mechanical linkage of the axes Δ1 and Δ2 is achieved, for example, by a common mounting ( Figure 1A ) is provided.

[0105] exist Figure 1B In the example of the characterization system 102 shown, the microscope objective 125 is contained in the full-field imaging device of the sighting device 110 and the optical axes Δ1 and Δ2 coincide. In this example, a beam splitter element 113 makes it possible to separate the detection paths of the sighting device and the microanalysis device.

[0106] In both examples, a distal end of the characterization system may be defined, which is configured to be in contact with or close to the biological tissue S. For example, in the case of characterization of the skin, the distal end of the characterization system may include a glass sheet (not shown in the figure) or a mechanical component (not shown in the figure) that makes it possible to set a predetermined distance between the skin and the entrance pupil of the microscope objective.

[0107] In these examples, the full-field illumination device 111 of the aiming device comprises a set of LEDs arranged on the distal end of the aiming device.

[0108] The microscopic analysis device 120 in each of these examples further comprises: an illumination path configured to illuminate the biological tissue with a first illumination pattern included in the elementary surface, for example, through a microscope objective; and a detection path comprising the microscope objective 125, the detection path configured to detect a light beam emitted by the biological tissue in response to the illumination of the biological tissue with a detection pattern included in the elementary surface and to generate microscopic analysis information. In the case of a specific example, with respect to Figure 2 The illumination path and detection path of the microscopic analysis device 120 are described.

[0109] exist Figure 1A and Figure 1B In the example shown, the microscopic analysis device 120 includes a mount 121 that generally includes at least some of the optoelectronic components of the detection path of the microscopic analysis device and may include at least some of the optoelectronic components of the illumination path. In some exemplary embodiments, the optoelectronic components may include, but are not limited to, one or another of the following: an illumination source, an interferometric assembly, a detector and associated processing unit, one or more optical systems for providing optical conjugation between image planes or between pupil planes, one or more mirrors, one or more scanning systems, etc. The arrangement of such optical components is known to those skilled in the art and depends on the microscopic analysis sought to be performed.

[0110] In some exemplary embodiments, some of the optoelectronic components may be located outside the mount 121, such as an illumination source 124 (e.g., a laser source), a detector 128 (e.g., a camera associated with a processing unit (not shown) for generating microscopic analysis information from detection signals), and optionally one or more controllers 122 for controlling the optoelectronic components arranged in the mount 121. The one or more controllers are, for example, electronic cards configured to control the axial displacement of the microscope objective 125 when necessary, or to control the reflective mirrors, light sources, etc. of the scanning system. The one or more controllers together with the illumination source 124 may be controlled by a control unit (not shown).

[0111] When the microscopic analysis device 120 is configured for confocal imaging and / or optical coherence tomography imaging, the microscopic analysis information includes at least one image of the biological tissue. When the microscopic analysis device 120 is configured for spectral analysis, the microscopic analysis information includes at least one spectrum of the light beam emitted by the illuminated biological tissue.

[0112] It should be noted that in other exemplary embodiments, the illumination source of the illumination path and / or the detector of the detection path may be integrated into the mount 121 .

[0113] exist Figure 1A and Figure 1B In the example shown, separate mounts 115 and 121 are shown. Of course, in practice, in some exemplary embodiments, a single mount may be provided for the arrangement of elements of the aiming device and the microscopic analysis device.

[0114] In some exemplary embodiments, for example Figure 3A As shown, additional mounting parts ( Figure 1A and Figure 1B ), to ensure mechanical linkage between the optical axes by firmly combining the mounts 121, 115, the microscope objective 125 and the full-field imaging device of the aiming device.

[0115] The characterization systems 101, 102 also include a processing unit having a first processing module 130 and a second processing module 140. The first processing module 130 is configured to determine at least one first biological tissue characterization parameter at a given number of points of the detection pattern from the microscopic analysis information. The second processing module 140 is configured to position each of a plurality of basic surface images relative to the surface image of the region of interest, and the plurality of basic surface images are continuously acquired by a full-field imaging device that moves the aiming path. For example, the surface image of the region of interest is obtained by mosaicking a plurality of basic surface images that are continuously acquired and have partial overlap by a full-field imaging device that moves the aiming path. The second processing module 140 is also configured to generate at least one first graphic element for the at least one first characterization parameter determined for each of the plurality of basic surface images.

[0116] The characterization system 101 , 102 further comprises a display module 150 configured to display an organizational diagram comprising at least said first diagram element, the diagram being positioned relative to said surface image, as will be explained in more detail below.

[0117] exist Figure 1B In the example of , the first optical axis and the second optical axis coincide, which simplifies the positioning of the at least one characterizing parameter map on the surface image. Figure 1A In an example, the mechanical linkage of the first optical axis and the second optical axis makes it possible to locate the detection pattern in the aiming image and thus to locate the map of the at least one characterizing parameter on the surface image of the region of interest.

[0118] Figure 2 A diagram illustrating an example of a system for characterizing a region of interest in biological tissue, performing optical coherence tomography microscopy, such as the LC-OCT pathway described in [Reference 4].

[0119] In this example, the characterization system 200 comprises a microscope objective 125 which is common to the microscopic analysis device 120 and the wide field imaging device 211 of the aiming device 110. In this example, one or more mountings are not shown. As in the previous examples, the characterization system 200 comprises processing modules 130, 140 (e.g. modules of a computer 240) and a display module 150.

[0120] Thus, in this example, the aiming device 110 comprises a microscope objective 125, a beam splitter element 113, a full-field illumination device 111 configured to illuminate biological tissue with a first spectral band, a two-dimensional detector 215 having a detection area 216, and a Figure 2 One or more imaging elements represented by elements 217, 218 and configured to form, together with the microscope objective 125, a full-field imaging device 211 that optically conjugates a given effective field of the sample with a detection area 216 of a two-dimensional detector 215. Thus, the sighting device makes it possible to form a surface reflection sighting image of an elementary surface whose size is defined by the effective field of view. The two-dimensional detector 215 is connected to a processing module 140 configured to combine a plurality of elementary surface images by mosaicking.

[0121] In this example, the full-field illumination device 111 comprises a plurality of light sources arranged on the distal portion of the characterization system 200, for example, arranged around the distal face of the microscope objective 125, and the full-field illumination device allows direct illumination of the biological tissue S. The light source is, for example, a light emitting diode emitting a wavelength between about 400 nm and about 800 nm. Of course, other illumination devices are also possible, such as a light source arranged upstream of the microscope objective and a beam splitter element (e.g., a beam splitter cube), the beam splitter element being configured to direct the illumination beam through the microscope objective toward the biological tissue.

[0122] exist Figure 2In the example of , the microscopic analysis device 120 includes an illumination path configured to illuminate a sample through a microscope objective 125 with a given illumination pattern and a second spectral band, which may be at least partially different from the first spectral band. The illumination path includes, for example, an illumination source 222 configured to emit a collimated light beam, a cylindrical reflection lens or a reflector 232 (for example, in the case of an LC-OCT path), a beam splitter element 233 (a beam splitter cube or a beam splitter plate), and a reflective element 234 (optionally) configured to send the illumination beam emitted by the illumination source to the microscope objective 125. In this example, the illumination path also includes: a scanning device 231 for scanning the illumination beam, the scanning device 231 being configured to scan the illumination beam in one or two dimensions; and a beam splitter element 113, the beam splitter element 113 being configured to separate the aiming path and the microscopic analysis path.

[0123] The illumination source 222 may include, for example, a (spatially) coherent, polychromatic, collimated light emission source. Optical devices and / or spatial filters (not shown) may collimate and / or coherent the light source and / or impart a specific spectral distribution to it. The central wavelength of the light source depends on the application, for example between 600nm and 1500nm, and the spectral width is, for example, between about 50nm and 250nm. In the case of LC-OCT applications (e.g., as described in reference 4), the illumination source 222 may, for example, include, but is not limited to, a supercontinuum laser that is spectrally filtered by an optical fiber to emit around about 800nm ​​and collimated by an off-axis parabolic mirror. In the case of full-field tomography or FF-OCT ("full-field OCT") (e.g., as described in the article by E. Beaurepaire et al. [reference 5]), the illumination source may be selected to be spatially incoherent, and the illumination source may include a device for full-field illumination of the sample, such as a Kohler illumination system.

[0124] Cylindrical optics 232 are optional, and allow microscopy using illumination along a line ("line field" microscopy).

[0125] The scanning device 231 for scanning the illumination beam can be configured for one-dimensional or two-dimensional scanning of a point or line in order to form in a known manner a cross-sectional image of the sample known as a "B-scan" (that is, in a plane parallel to the optical axis of the microscope objective), a cross-sectional image or a frontal image of the sample known as a "C-scan" (that is, in a plane perpendicular to the optical axis of the microscope objective), or a 3D image of the sample resulting from the acquisition of multiple B-scan images or C-scan images. As previously mentioned, the scanning device can include one or more scanning elements selected from the following elements: a galvanometer, a polygon mirror, an electro-optical deflection system or an acousto-optical deflection system, or a combination of these various elements (in the case of two-dimensional scanning). The scanning device can also include an optical device for conjugating at least one of the scanning elements with the entrance pupil of the microscope objective 125, for example in order to avoid vignetting.

[0126] The detection path of the microscopic analysis device is configured to detect a light beam emitted by the sample in response to said illumination of the sample with a given detection pattern. In this specific (non-limiting) example, the detection path includes an interferometer for performing optical coherence tomography microscopy. More precisely, the interferometer includes an object arm with a microscope objective 125, a scanning device 231 and a reflective element or partially reflective element 113, 234, 233, which is configured to send a light beam emitted by the sample S in response to said illumination of the sample to a detector 128.

[0127] The interferometer of the detection path also includes a reference arm, which in this example is separated from the object arm by a beam splitter cube 233 and includes in a known manner: a microscope objective 238 (optional), for example similar to the microscope objective 125, for performing dispersion compensation; a dispersion compensation system (optional, not shown); a reference mirror 235; a platform 239 (optional), which is configured to displace the reference mirror 235, for example, if it is necessary to modulate the optical path on the reference arm. In this example, the detection path also includes an objective 236, which is configured to optically conjugate the plane of the sample S with the detection area of ​​the detector 128 together with the microscope objective.

[0128] In this example, the detector 128 comprises an optical sensor having a detection area and may also include a spatial filter for confocal detection (if this cannot be provided by the size of the detection area) and / or a spectral filter for limiting the detected wavelength band. In the case of a point scanning system, the sensor may comprise an elementary detection surface (e.g. a photodiode), in the case of a "line field" system, the sensor may comprise a one-dimensional sensor (e.g. a linear camera), or the sensor may comprise a two-dimensional sensor in which only the area of ​​interest is taken into account for use as an elementary detection area or a one-dimensional sensor. In the case of FF-OCT applications, a two-dimensional sensor may be used in a conventional manner.

[0129] During operation, interference is generated in the detection area of ​​the detector 128 between light from the reference arm and light backscattered by the sample illuminated with the illumination pattern, optionally and in a known manner, by modulation of the optical path difference between the reference arm and the object arm of the sample, in order to form a tomographic image, in particular a frontal image.

[0130] A processing unit (not shown) receives the detection signals generated by the detector 128 and resulting from the interferometric detection in a known manner and is configured to reconstruct a microscopic image, such as a 2D cross-sectional image (B-scan or C-scan), from the detection signals. The processing unit may be connected to a storage unit (not shown) for storing the generated images and / or videos. The processing unit is connected to a processing module 130 configured to generate a tissue map at a given number of points of the detection pattern from the tomographic image based on at least one biological tissue characterization parameter, as will be explained in more detail below.

[0131] Such a microscopic analysis device 120 thus operates like an optical coherence tomography microscopy approach known in the art.

[0132] although Figure 2 A specific example is shown in FIG. 1 , but a person skilled in the art will understand that the characterization system according to the present description is applicable to any known components used in the prior art for optical coherence tomography microscopy, Figure 2 The optomechanical elements shown in can be adapted accordingly or more generally to any known components for microscopic analysis of biological tissue.

[0133] Figure 3A A diagram illustrating the steps of a method for characterizing a region of interest ROI of biological tissue S surrounding a suspicious area 350 , such as a suspicious area visible on the skin, implemented by a characterization system 300 according to the present description, is shown in simplified form.

[0134] As in the previous examples, the characterization system 300 generally includes a sighting device 110 and a microscopic analysis device 120 , in which example the widefield imaging device of the sighting device 110 includes a microscope objective 125 of the microscopic analysis device, and the sighting path and the microscopic analysis path are separated by a beam splitting element 113 .

[0135] In this example, at least some elements of the aiming device and the microscopic analysis device, including the microscope objective 125, are contained within a common mounting 301 provided with a handle 302 and referred to in this specification as a "probe". As previously mentioned, such a probe may be defined at a distal end intended to contact or approach the tissue sought to be characterized, such as the skin.

[0136] During operation, the aiming device 110 makes it possible to continuously acquire reflection images 311 of the elementary surface or “aiming images” of said region of interest ROI of the biological tissue S, which in some exemplary embodiments are acquired continuously and possibly with partial overlap.

[0137] For each of the plurality of aiming images thus acquired, a microscopic analysis of the biological tissue is performed at the level of the aiming image by the microscopic analysis device 120. As described above, the microscopic analysis includes, for example, illuminating the biological tissue with a first given illumination pattern included in the elementary surface by means of a microscope objective, and detecting a light beam emitted by the biological tissue in response to the illumination of the biological tissue with a detection pattern 321 included in the elementary surface, so as to generate microscopic analysis information, for example, an image (in Figure 3A not shown).

[0138] Thus, the characterization method comprises processing said microscopic analysis information in order to determine, for each targeting image, at least one first biological tissue characterization parameter at a given number of points of said detection pattern.

[0139] Characteristic parameters include, but are not limited to, morphological measurements (e.g., the presence of cells and tissue structures in each layer of the tissue, the thickness of one or more layers of the tissue), cytological measurements (e.g., the density of cell nuclei, such as the density of keratinocyte nuclei or collagen anisotropy), optical measurements (optical density, fluorescence measurements, multiphoton measurements), chemical measurements (Raman measurements, spectral measurements), mechanical measurements (e.g., elastography), or a combination of these measurements.

[0140] Characterization parameters may also include, for example, scores (possibly weighted) based on one or more of the above measurements. For example, such scores may express the presence or absence of a lesion in a tissue in a binary or non-binary form (e.g., by a probability percentage, a number on a given scale, or a binary result).

[0141] So, as an example, in Figure 3A In the example of FIG. 3 , the characterization parameter expresses the presence 332 or absence 331 of a lesion in the tissue.

[0142] According to some examples, the definition of the characterization parameters may also result in the same property being detected at multiple different depths. For example, one characteristic parameter may be "the probability of a lesion being measured at a depth greater than 100 μm is greater than 70%".

[0143] It should be noted that the decision regarding the presence / absence or probability percentage of a lesion may be generated by artificial intelligence via an artificial intelligence module (e.g. based on a deep learning model using a neural network).

[0144] In general, such an artificial intelligence module can be used to determine characterization parameters based on one or a combination of the above-mentioned measurements.

[0145] Thus, in some exemplary embodiments, based on a neural network, a deep learning model can be used for cross-sectional images (B-scan or C-scan) to detect the presence of pathology (e.g., basal cell carcinoma (BCC)) with a score between 0 and 1. All these scores (front view visualization) form a map of pathological and healthy areas. The exact boundaries can be calculated using thresholds that define risk areas.

[0146] In one exemplary embodiment, training a binary output The ResNet-18 model was developed to predict the presence or absence of BCC in vertical cross-sectional images (B-scans). The model was used to predict the detection score of 0 and 1 in each 3D image. To limit the risk of underestimating the lesion, a threshold can be used to define the pathological area. The convex envelope of this area can be considered as the area that the doctor wants to remove.

[0147] In another exemplary embodiment, training a binary output The EfficientNet-B3 model was developed to predict the presence or absence of BCC in vertical cross-sectional images (B-scans). The model is used to predict the detection score of 0 and 1 in each 3D image. In order to limit the surgical area as much as possible, a threshold can be used to define the pathological area. The convex envelope of this area can be considered as the area that the doctor wants to remove.

[0148] Furthermore, it is entirely possible to have multiple characterization parameters at one point (if multiple characterization parameters are verified at that point), such as "probability of lesion greater than 70%" and "epidermal thickness less than 100 μm").

[0149] therefore, Figure 3B A first example of the display of a surface image 360 ​​of a region of interest ROI obtained using the method according to the present description is shown, on which a suspicious area 350 is visible. Figure 4A , Figure 4B and Fig. 6A , Figure 6B , which describes some examples of the method in more detail. Superimposed on the surface image 360, a first graphical element 361 for a first characterization parameter, a second graphical element 362 for a second characterization parameter, and a third graphical element 363 for a third characterization parameter can be seen.

[0150] It should be noted that in some exemplary embodiments, the surface image of the region of interest (not shown in the figure) can be an image obtained by mosaicking the aiming image, or can be obtained by another independent imaging system. Such another surface image of the region of interest is, for example, a dermoscopic image obtained by a dermoscopy.

[0151] It should also be noted that, particularly in the case where a surface image of a region of interest is obtained by mosaicking the aiming image, one or more image elements may be superimposed on another surface image obtained by another imaging system and positioned relative to the surface image of the ROI obtained by mosaicking.

[0152] Figure 3C A second example of the display of a surface image 360 ​​of a region of interest ROI obtained using the method according to the present description is shown, on which a suspicious area 350 is visible.

[0153] In this example, the display module is configured so that the tissue diagram according to one or more parameters is displayed in a manner juxtaposed with the surface image of the region of interest. Then, the display module is configured to additionally show a common cursor 380 in the tissue diagram and in the surface image of the region of interest. The common cursor can be a graphical element configured to locate the same spatial position on the diagram and on the surface image of the region of interest.

[0154] As previously mentioned, the surface image of the region of interest may be an image obtained by mosaicking the aiming image, or a surface image obtained by an independent system, such as a dermoscopic image.

[0155] Figure 4A A block diagram illustrating a first example of a characterization method according to the present description is shown in detail, in particular for implementing the method in in vivo characterization of biological tissue. The method is implemented, for example, using a characterization system of the type described with reference to the previous figures. Figure 5A , Figure 5B and Figure 5C Shown as Figure 4A The steps of the method described in .

[0156] like Figure 3AAs shown, in the case where the characterization system forms a probe 300 that can be manipulated by an operator, such as in the case of in vivo characterization, the method comprises a step 401 of positioning the probe 300 in a region of interest ROI of the biological tissue S to be characterized. Of course, in ex vivo characterization, the relative displacement of the characterization system and the sample can also be performed by moving the sample.

[0157] Then, the method comprises successively acquiring 411 , 412 reflection images of a basic surface of the region of interest of the biological tissue by means of a targeting device comprising a two-dimensional detector and a full-field imaging device as described above, the successively acquired basic surface images having partial overlap.

[0158] For example, Figure 4A As shown, the method includes (step 411) acquiring and displaying a first image of the base surface (referred to as a "targeting image" in this specification) by a display module. Next, if the region of interest is not completely covered (step 431), the operator can move the probe (412) to image a base surface different from the base surface previously imaged by the targeting device.

[0159] like Figure 5A As shown, the displacement may start from the suspicious region 350 visible to the physician and then move away from the region 350 so as to cover the entire region ROI.

[0160] like Figure 4A As shown, for each of the plurality of elementary surface images thus obtained, a microscopic analysis 421 of the biological tissue is performed at the level of the elementary surface by the microscopic analysis device as described above. The microscopic analysis specifically includes illuminating the biological tissue with a first given illumination pattern included in the elementary surface, and detecting a light beam emitted by the biological tissue in response to the illumination of the biological tissue with a detection pattern included in the elementary surface to generate microscopic analysis information.

[0161] Then, the method comprises processing the microscopic analysis information so as to determine at least one first biological tissue characterization parameter at a given number of points of the detection pattern from the microscopic analysis information and generating a map element for the characterization parameter. Figure 4A As shown, more specifically, this step can include an analysis step 422, in which it is sought to identify at least one detection area of ​​the detection pattern for which the microscopic analysis information corresponds to at least one characterizing parameter. If this is the case, one or more areas are recorded (step 423) within the corresponding aiming image.

[0162] therefore, Figure 5BFor example, a number of aiming images are shown (see, for example, 311, 312), for which the detection patterns are detection lines respectively indicated by 321, 322. White and grey areas respectively represent different characterization parameters.

[0163] Then, in this example, the method comprises combining the surface images of the plurality of surface images by mosaicking 441 in order to obtain a surface image of the region of interest. The combination by mosaicking can be performed using known techniques, including, for example, a registration step and a stitching step, such as described in the article by J. Ogien et al. [Reference 6].

[0164] therefore, Figure 5C A surface image of the region of interest obtained by combining the aiming images through mosaicking is shown.

[0165] Furthermore, at least one graphical element corresponding to at least one parameter is reconstructed (step 451 ) from the recorded area for at least a plurality of targeting images.

[0166] Then, the surface image of the region of interest and the map comprising at least the map element, which is positioned in the surface image, may be displayed (step 461). Figure 3B or Figure 3C shown.

[0167] therefore, Figure 5D A surface image 360 ​​of a region of interest ROI is schematically shown, on which surface image 360 ​​graphic elements 361 are superimposed, for example corresponding to areas identified as “having lesions” based on the microscopic analysis information in each targeting image.

[0168] Figure 4B A diagram illustrating a block diagram of a second example of a characterization method according to the present description is shown.

[0169] In this example, with Figure 4A Similar steps are identified by the same reference numerals.

[0170] In this example, the method no longer has to include the step of mosaicking the aiming image. Here, the method includes a step 442 of positioning each aiming image of the plurality of aiming images in the surface image of the region of interest. The surface image of the region of interest may be an image obtained by mosaicking. It may also be an image obtained by an independent system, such as a dermoscopic image obtained by a dermatoscope. Then, step 462 may include displaying the surface image of the region of interest, the surface image being superimposed on the map comprising at least the map element.

[0171] Fig. 6AA diagram illustrating a block diagram of a third example of a characterization method according to the present description is shown in detail.

[0172] As previously described, the method comprises a step 601 of positioning a probe in a region of interest of biological tissue sought to be characterized.

[0173] Then, the method comprises successively acquiring 611 , 612 reflection images of a basic surface of the region of interest of the biological tissue by means of a targeting device comprising a two-dimensional detector and a full-field imaging device as described above, the successively acquired basic surface images having partial overlap.

[0174] However, in this example, the method includes, after each step 611 of an image of the base surface or "sighting image", reconstructing 641 the image by mosaicking from the acquired sighting image and from mosaicking results reconstructed up to that point during the method, and displaying 642 the image mosaicking result. As previously mentioned, mosaicking is performed using known techniques, such as those described in [Reference 6].

[0175] Next, if the region of interest is not completely covered (step 651), the operator may move the probe (612) to image a different base surface than that previously imaged by the targeting device.

[0176] like Fig. 6A As shown, for each elementary surface image in a plurality of acquired elementary surface images, a microscopic analysis 621 of the biological tissue is performed at the level of the elementary surface by the microscopic analysis device as described above. The microscopic analysis specifically includes illuminating the biological tissue with a first given illumination pattern included in the elementary surface, and detecting a light beam emitted by the biological tissue in response to the illumination of the biological tissue with a detection pattern included in the elementary surface to generate microscopic analysis information.

[0177] Then, the method comprises processing the microscopic analysis information so as to determine at least one first biological tissue characterization parameter at a given number of points of the detection pattern from the microscopic analysis information, and generating a map element for the characterization parameter from a plurality of targeting images. Fig. 6A As shown, more specifically, this step can include an analysis step 622, in which it is sought to identify at least one detection area of ​​the detection pattern for which the microscopic analysis information corresponds to at least one characterizing parameter. If this is the case, one or more areas are recorded (step 623) within the corresponding aiming image.

[0178] exist Fig. 6A In the example of the method shown, at least one graphical element corresponding to at least one parameter is reconstructed from the recorded areas for a plurality of targeting images.

[0179] However, in this example, more specifically, in the case 631 where an area associated with the characterization parameters has been recorded, the method comprises a step 632 of reconstructing at least one graphical element corresponding to at least one characterization parameter from said recorded area.

[0180] The method also includes displaying (step 633) one or more graphical elements reconstructed thereby, for example, superimposed on the mosaic of images as described above or alternatively in juxtaposition. Thus, during the characterization method, the user sees the image mosaic that is formed and grows as new aiming images are acquired, as well as one or more graphical elements superimposed on or juxtaposed with the mosaic.

[0181] Thus, the physician can have an overview of the acquisition situation and thus better position himself, for example, relative to a large field image previously acquired by dermoscopy. Furthermore, this configuration also potentially allows the map to be displayed in real time and thus allows the physician to make decisions based on the map during acquisition (e.g., proceed in a specific direction during acquisition, or stop in order to directly extract a portion of tissue based on the information provided by the map).

[0182] When the entire area has been covered (step 651), the image of the region of interest obtained by mosaicking during the method is recorded (step 661), and at least one map corresponding to at least one characterization parameter is reconstructed (step 662) from the map elements recorded during the method.

[0183] Figure 6B A diagram illustrating a block diagram of a fourth example of a characterization method according to the present description is shown.

[0184] In this example, with Fig. 6A Similar steps are identified by the same reference numerals.

[0185] In this example, the method no longer has to include the step of mosaicking the aiming image. Here, the method includes a step 643 of positioning the aiming image in the surface image of the region of interest. The surface image of the region of interest may be an image obtained by an independent system, such as a dermoscopic image obtained by a dermatoscope. Step 634 may include displaying the reconstructed image element on the surface image of the region of interest.

[0186] Although they have been described by means of a certain number of exemplary embodiments, the systems and methods for characterizing a region of interest in biological tissue according to the present specification include various variations, modifications and improvements that are obvious to those skilled in the art, and it should be understood that these variations, modifications and improvements form part of the scope of the invention as defined by the appended claims.

[0187] References

[0188] Reference 1: A.Alfonso-Garcia et al. "Real-time augmented reality fordelineation of surgical margins during neurosurgery using autofluorescence lifetime contrast", J Biophotonics, 2020Jan; 13(1):e201900108

[0189] Reference 2: WO2020087164

[0190] Reference 3: S.Li, "A review of feature detection and match algorithms for localization and mapping", IOP Conference Series: Materials Science and Engineering vol.231 012003(2017)

[0191] Reference 4: Published patent application WO2015092019

[0192] Reference 5: E. Beaurepaire et al. "Full-field optical coherence microscopy" Opt. Lett. 23, 244-246 (1998)

[0193] Reference 6: Ogien et al., "Video-mosaicking of human skin in vivo using handheld line-field confocal optical coherence tomography" Proc.SPIE 11211, Photonics in Dermatology and Plastic Surgery 2020,1121114(19February 2020)

[0194] Reference 7: WO2022017784

Claims

1. A system for characterizing a region of interest of a biological tissue, the system comprising: - a sighting device (110), the sighting device (110) comprising: - a full-field illumination device (111) configured to illuminate the biological tissue with a first spectral band; - a two-dimensional detector (215), the two-dimensional detector (215) comprising a detection area (216); - a full-field imaging device (211) comprising a first optical axis (Δ1) and configured to bring an elementary surface of the biological tissue into optical conjugation with the detection area of ​​the two-dimensional detector, the two-dimensional detector generating a reflected image of the elementary surface during operation; - A microscopic analysis device (120), the microscopic analysis device (120) comprising: - a microscope objective (125) comprising a second optical axis (Δ2) mechanically integrated with said first optical axis; - an illumination path configured to illuminate said biological tissue with a first illumination pattern included in said elemental surface and with a second spectral band; a detection path comprising said microscope objective, said detection path being configured to detect a light beam emitted by said biological tissue in response to illumination of said biological tissue with a detection pattern comprised in said elemental surface and to generate microscopic analysis information; - a data processing unit, the data processing unit comprising: - a first processing module (130) configured to determine at least one first biological tissue characterization parameter at a given number of points of the detection pattern from the microscopic analysis information; - a second processing module (140) configured to - position each of a plurality of elementary surface images relative to the surface image of the region of interest, the plurality of elementary surface images being successively acquired by moving the full-field imaging device of the aiming device; and - generating at least one first image element for the at least one first biological tissue characterization parameter from the at least one first biological tissue characterization parameter determined for at least some of the plurality of elementary surface images; - a display module (150) configured to display a tissue map comprising at least said first map element, said map being positioned relative to said surface image of said region of interest.

2. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The elementary surface images have partial overlap, and the surface image of the region of interest is generated by mosaicking the plurality of elementary surface images.

3. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The surface image of the region of interest is generated by an imaging system independent of the system for characterizing the region of interest of biological tissue.

4. A system for characterizing a region of interest of biological tissue according to claim 2 or 3, wherein: The display module is configured to additionally display the surface image of the region of interest or another surface image of the entire region of interest, which is acquired by another imaging system and is positioned relative to the surface image of the entire region of interest.

5. The system for characterizing a region of interest of biological tissue according to claim 4, wherein: The display module is configured to display the tissue map in a manner of being superimposed on the surface image of the region of interest or on the other surface image of the entire region of interest.

6. The system for characterizing a region of interest of biological tissue according to claim 4, wherein: The display module is configured so that the tissue map is displayed in juxtaposition with the surface image of the region of interest or with the other surface image of the entire region of interest, and the display module is configured to additionally show a common cursor in the tissue map and in the surface image of the region of interest or in the tissue map and in the other surface image of the entire region of interest.

7. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The full-field imaging device of the sighting device comprises the microscope objective.

8. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The at least one first biological tissue characterization parameter comprises a parameter selected from the following: morphological measurements, cytological measurements, optical measurements, measurements characterizing chemical composition, mechanical measurements, combinations of these measurements, scores for characterizing tissue state, such as scores based on at least one of these measurements.

9. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The first module for processing the microscopic analysis information includes an artificial intelligence module based on a deep learning model using a neural network.

10. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The microscopic analysis device is configured for confocal imaging and / or optical coherence tomography imaging, and the biological tissue microscopic analysis information includes at least one image of the biological tissue.

11. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The microscopic analysis device is configured for spectral analysis, and the biological tissue microscopic analysis information comprises at least one spectrum of the light beam emitted by the biological tissue.

12. The system for characterizing a region of interest of biological tissue according to claim 1, wherein: The second processing module is further configured to determine a resection margin of the region of the biological tissue to be extracted based on the at least one first image element.

13. A computer program product comprising program code instructions for implementing a data processing method for characterizing a region of interest (ROI) of a biological tissue (S) when said program is executed on a computer, said data being obtained by a characterization system, said characterization system comprising: - a sighting device (110), the sighting device (110) comprising: - a full-field illumination device (111) configured to illuminate the biological tissue with a first spectral band; - a two-dimensional detector (215), the two-dimensional detector (215) comprising a detection area (216); - a full-field imaging device (211) comprising a first optical axis (Δ1) and configured to bring an elementary surface of the biological tissue into optical conjugation with the detection area of ​​the two-dimensional detector, the two-dimensional detector generating a reflected image of the elementary surface during operation; - A microscopic analysis device (120), the microscopic analysis device (120) comprising: - a microscope objective (125) comprising a second optical axis (Δ2) mechanically integrated with said first optical axis; - an illumination path configured to illuminate said biological tissue with a first illumination pattern included in said elemental surface and with a second spectral band; a detection path comprising said microscope objective, said detection path being configured to detect a light beam emitted by said biological tissue in response to illumination of said biological tissue with a detection pattern comprised in said elemental surface and to generate microscopic analysis information; The data processing method comprises the following steps: - determining from said microscopic analysis information at least one first biological tissue characterizing parameter at a given number of points of said detection pattern; - positioning each elementary surface image of a plurality of elementary surface images, which are successively acquired by moving the full-field imaging device of the targeting device, relative to the surface image of the region of interest; and - generating at least one first map element for the at least one first biological tissue characterization parameter from the at least one first biological tissue characterization parameter determined for at least some of the plurality of elementary surface images.

14. The computer program product of claim 13, wherein: The elementary surface images have partial overlap, and the surface image of the region of interest is generated by mosaicking the plurality of elementary surface images.

15. A computer program product according to claim 13 or 14, wherein: The at least one first biological tissue characterization parameter is determined by an artificial intelligence module based on a deep learning model using a neural network.

16. A non-transitory computer-readable storage medium storing the computer program product according to any one of claims 13 to 15.

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