Method and system for obtaining sufficient cytological images in cytopathology

Abstract

Exemplary methods and systems for identifying suspect cytological specimens containing target cells are disclosed. One exemplary method includes acquiring a first image associated with a first region of the cytological specimen through a first objective lens, acquiring a second set of one or more images associated with a first subregion of the first region through a second objective lens, identifying a first number of target cells, and determining whether the first number of target cells is greater than a threshold number. If the first number of target cells is greater than the threshold number, the exemplary method prompts a warning. If not, the exemplary method further includes acquiring a third set of one or more images associated with a second subregion through a second objective lens, and identifying a second number of target cells associated with the second subregion.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Application No. 63 / 403,660, filed September 2, 2022, which is incorporated herein by reference in its entirety.

[0002] FIELD OF THE INVENTION Embodiments of the present invention generally relate to methods and systems for acquiring cytological images in cytopathology. [Background technology]

[0003] Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims of this application and are not admitted to be prior art by inclusion in this section.

[0004] Cytopathology is a branch of pathology that studies and diagnoses disease at the cellular level and generally involves obtaining cytological images at the cellular level. Obtaining cytological images of a cytological specimen involves image digitization of a glass slide on which the cytological specimen is distributed. Image digitization typically involves scanning the glass slide of the cytological specimen to generate a digital slide of the image of the glass slide. The scanning may be performed by a whole slide image scanner. The digital slide of the image can be displayed on a display (e.g., a computer monitor) instead of a microscope. Summary of the Invention [Problem to be solved by the invention]

[0005] Efficient and accurate image digitization of glass slides can be challenging. One reason is that cytology specimens on glass slides may contain single cells and cell clusters distributed in three-dimensional space. The three-dimensional distribution of cells and cell clusters within a cytology specimen makes focusing difficult. One conventional method to address this focusing difficulty is to acquire a focused image of the entire glass slide, but this method is very time-consuming. Another conventional method to address this focusing difficulty is to manually mark an area on the glass slide for focusing and then acquire a focused image of the marked area. However, the manual approach may mark the wrong area, and as a result, the acquired focused image may not include many of the target cells in the cytology specimen. [Brief explanation of the drawings]

[0006] [Figure 1] An example of the distribution of cytology specimens in three-dimensional space on a glass slide. [Figure 2] 1 illustrates how an example configuration of a multiple objective module captures images related to layers of a cytology specimen through a first objective. [Figure 3A] A diagram showing an example of a part of a cytology specimen [Figure 3B] FIG. 1 shows an image relating to a portion of a cytology specimen obtained through a first objective lens. [Figure 4A] 10 illustrates how an example configuration of a multiple objective module captures an image related to a second sub-portion of the cytology specimen through a second objective. [Figure 4B] FIG. 10 shows an image relating to a second sub-portion obtained through a second objective lens. [Figure 4C] 10 illustrates how an example configuration of a multiple objective module acquires images relating to multiple second sub-portions of a cytology specimen through a second objective. [Figure 4D] FIG. 10 shows an image relating to a second sub-portion obtained through a second objective lens. [Figure 4E]10 illustrates how an example configuration of a multiple objective module captures an image related to a second sub-portion of the cytology specimen through a second objective. [Figure 4F] FIG. 2 shows an image relating to a second sub-portion obtained by a second objective lens 220. [Figure 5] FIG. 1 illustrates a system for acquiring images associated with a cytology specimen. [Figure 6] 1 is a flowchart illustrating a process for acquiring images related to target cells distributed in a cytology specimen. [Figure 7] 1 is a flowchart illustrating an exemplary process for acquiring images associated with target cells distributed in a cytology specimen, arranged in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0007] In the following detailed description, reference is made to the accompanying drawings, which form a part of this specification. In the drawings, like symbols generally refer to like elements unless the context dictates otherwise. The exemplary embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described and illustrated in the figures herein, can be arranged, substituted, combined, and designed in a variety of different configurations, all of which are within the scope expressly contemplated herein. As used herein, a cytology specimen is considered suspicious if target cells distributed within the space of the cytology specimen include cells at risk for disease.

[0008] 1 is a diagram illustrating a cytology specimen 110 distributed within a three-dimensional space of the cytology specimen 110 on a glass slide 120, arranged in accordance with some embodiments of the present disclosure. The cytology specimen 110 may include a plurality of cells and impurities. For example, the cytology specimen 110 may include dust or blemishes 131, target cells 141 (e.g., epithelial cells at risk of malignancy), and non-target cells 151, 153, 155, 157, and 159 (e.g., red blood cells, normal epithelial cells, and other cells). The dust or blemishes 131, the target cells 141, and the non-target cells 151, 153, 155, 157, and 159 are distributed within a three-dimensional space on the glass slide 120 (i.e., at different depths within the volume of the cytology specimen 110).

[0009] FIG. 2 illustrates how an example configuration of a multiple objective lens module 200, arranged in accordance with some embodiments of the present disclosure, acquires images related to layers of a cytology specimen 210 through a first objective lens 220. In relation to FIG. 1 , in FIG. 2, the cytology specimen 210 may correspond to the cytology specimen 110. In FIG. 2, the multiple objective lens module 200 includes a first objective lens 220 configured to acquire images related to the cytology specimen 210. More specifically, the first objective lens 220 is configured to acquire images related to one or more layers of the cytology specimen 210. In some embodiments, each layer may correspond to a depth of field associated with the field of view of the first objective lens 220.

[0010] In some embodiments, the first objective lens 220 has a first field of view, a first depth of field, and a first magnification, which may correspond to any of regions A, B, C, D, E, F, G, H, I, and J of the cytology specimen 210.

[0011] In some embodiments, the first depth of field refers to the distance between the closest object that is sharp and in focus relative to the first objective lens 220 and the farthest object that is sharp and in focus relative to the first objective lens 220 in the first field of view. Examples of distances may be Distance 1, Distance 2, and Distance 3 shown in FIG. 2. The first depth of field may correspond to a layer of the cytology specimen 210. Thus, Distance 1 may correspond to the first layer of the cytology specimen 210, Distance 2 may correspond to the second layer of the cytology specimen 210, and Distance 3 may correspond to the third layer of the cytology specimen 210.

[0012] The first layer may be the layer farthest from the glass slide (e.g., glass slide 120), and the third layer may be the layer adjacent to the glass slide (e.g., glass slide 120). Note that while FIG. 2 shows three layers, the cytology specimen 210 may include more or fewer layers depending on the first depth of field of the first objective lens 220. In these embodiments, the regions and distances may define portions of the cytology specimen 210. For example, J3 corresponds to the lower right anterior portion of the cytology specimen 210. Thus, in one embodiment shown in FIG. 2, the cytology specimen 210 may be defined by portions A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3, E1, E2, E3, F1, F2, F3, G1, G2, G3, H1, H2, H3, I1, I2, I3, J1, J2, and J3.

[0013] In some embodiments, the cytology specimen 210 may include, but is not limited to, dust 211, blemishes 212, a first type of cells 213 (e.g., epithelial cells at risk of malignancy), a second type of cells 214 (e.g., red blood cells), a third type of cells 215 (e.g., normal epithelial cells), and a fourth type of cells 216 (e.g., other cells). The dust 211, blemishes 212, the first type of cells 213, the second type of cells 214, the third type of cells 215, and the fourth type of cells 216 may be distributed within the cytology specimen 210 based on their specific gravities. The blemishes 212 may be manually marked by a user on the top surface of the cytology specimen 210. The dust 211 and the cells 213, 214, 215, and 216 may have significant differences in their densities and may be distributed in different layers of the cytology specimen 210. For example, dust 211 may have a lower specific gravity than cells 213, 214, 215, and 216 and may be distributed in a first layer of cytological specimen 210. Cells 213, 214, 215, and 216 may have a higher specific gravity than dust 211 and may be distributed in a second layer of cytological specimen 210.

[0014] In some embodiments, cells 213, 214, 215, and 216 in cytology specimen 210 typically have a size ranging from about 5 micrometers to about 20 micrometers. Note that the selection of first objective lens 220 may be based on a first depth of field of first objective lens 220. Because the first depth of field of first objective lens 220 may be much larger than the size of cells 213, 214, 215, and 216 in cytology specimen 210, images of cells 213, 214, 215, and 216 may be acquired at the same first depth of field (e.g., Distance 1, Distance 2, or Distance 3).

[0015] In some embodiments, before acquiring images of all layers of the cytology specimen 210 through the first objective lens 220, the first objective lens 220 is configured to acquire images of each layer (e.g., the first, second, and third layers) of several regions randomly selected from all of regions A, B, C, D, E, F, G, H, I, and J. For example, the first objective lens 220 may be configured to acquire images of portions H1, H2, and H3 within the randomly selected region H. Here, region H corresponds to a first field of view of the first objective lens 220, and distances 1, 2, or 3 correspond to a first depth of field of the objective lens 200. Several regions other than region H, for example, regions F, I, and J, may also be randomly selected. Thus, the first objective lens 220 may also acquire images of portions F1, F2, F3, I1, I2, I3, J1, J2, and J3 of the cytology specimen 210.

[0016] In some embodiments, the images of portions F1, F2, F3, H1, H2, H3, I1, I2, I3, J1, J2, and J3 are processed by a processor including an artificial intelligence engine to identify whether the images of portions F1, F2, F3, H1, H2, H3, I1, I2, I3, J1, J2, and J3 contain images of cells 213, 214, 215, and 216. The artificial intelligence engine may include machine learning capabilities. The artificial intelligence engine may be trained based on sample images of known cells corresponding to cells 213, 214, 215, and 216 having different contrast, brightness, shape, and other image characteristics. In some embodiments, for illustrative purposes only, the artificial intelligence engine may identify that images of cells 213, 214, 215, and 216 are primarily contained in images of portions F2, H2, I2, and J2, rather than in images of portions F1, F3, H1, H3, I1, I3, J1, and J3. Thus, the artificial intelligence engine may conclude that cells 213, 214, 215, and 216 are distributed in a second layer of cytology specimen 210. After reaching such a conclusion, first objective lens 220 is configured to acquire images of the entire second layer of cytology specimen 210 (i.e., images of portions A2, B2, C2, D2, E2, F2, H2, I2, and J2).

[0017] 2, FIG. 3A illustrates a portion 310 of cytology specimen 210, and FIG. 3B illustrates an image 330 associated with portion 310 acquired through first objective lens 220 configured in accordance with some embodiments of the present disclosure. In some embodiments, portion 310 may correspond to portion H2 of cytology specimen 210, and cells 321, 322, 323, 324, 325, and 326 may be distributed within portion 310. In some embodiments, cell 324 may be an epithelial cell at risk of malignancy, and cells 321, 322, 323, 325, and 326 may include normal epithelial cells, red blood cells, and other cells.

[0018] In some embodiments, image 330 may be a top view of portion 310 because first objective lens 220 is located on top of portion 310. Image 330 in Figure 3B has an image region H' that corresponds to region H associated with portion 310 in Figure 3A. The correspondence between region H and image region H' may include one or more factors related to magnification, reduction, rotation, and twist.

[0019] In some embodiments, the artificial intelligence engine is used to process image 330 based on contrast, brightness, shape, and other image characteristics associated with cells 321-326 to identify images 331, 333, and 335 in image 330, including images of cells 321-326. For example, images associated with cells 321 and 322 are identified for inclusion in image 331, images associated with cells 323, 324, and 326 are identified for inclusion in image 333, and images associated with cells 321, 322, and 325 are identified for inclusion in image 335. Images 331, 333, and 335 all have a spatial relationship within image region H' of image 330. Based on the spatial relationship between images 331, 333, and 335 and image region H', and the correspondence between image region H' and region H, referring to FIG. 3A , subregions 341, 343, and 345 within region H can be identified. In some embodiments, subregions associated with cells distributed within any of portions A2, B2, C2, D2, E2, F2, H2, I2, and J2 may be identified in a similar manner. In some embodiments, subregions 351, 353, and 355 of portion 310 are defined by distance 2 and subregions 341, 343, and 345, respectively. In some embodiments, as described above, cell 324 may be an epithelial cell at risk of malignancy (i.e., a target cell), and cells 321, 322, 323, 325, and 326 may include normal epithelial cells, red blood cells, and other cells (i.e., non-target cells), and the subregions associated with the target cells (i.e., subregions 351 and 353) may be examined in more detail than the subregions associated with the non-target cells (i.e., subregion 355). Subregions 351, 353, and 355 are further examined with a second objective to obtain more detailed images associated with target cell 324. For example, the second objective lens may be configured to capture images associated with subportions 351 and 353 rather than subportion 355. For example, the second objective lens may be configured to capture images associated with subportions 351 and 353, but not to capture images associated with subportion 355.

[0020] In some embodiments, as described above, subportions of portions A2, B2, C2, D2, E2, F2, G2, I2, and J2 may be further examined with a second objective to obtain more detailed images relating to cells 321 to 326.

[0021] 4A illustrates how an exemplary multiple-objective module, arranged in accordance with some embodiments of the present disclosure, acquires images related to a second sub-portion of a cytology specimen through a second objective lens. In FIG. 4A, second objective lens 410 of the multiple-objective module is configured to acquire one or more images related to sub-portion 420. With reference to FIG. 3A, sub-portion 420 may correspond to sub-portion 353 defined by distance 2 and sub-area 343.

[0022] In some embodiments, the second objective lens 410 has a second field of view, a second depth of field, and a second magnification. In some embodiments, with reference to FIG. 3A , the second field of view may correspond to subregion 343. In other embodiments, with reference to FIG. 3A , the second field of view may correspond to only a portion of subregion 343. For purposes of illustration only, FIG. 4A shows the second field of view as corresponding to subregion 343.

[0023] In some embodiments, the second magnification is greater than the first magnification. For illustrative purposes only, the first and second magnifications may be, but are not limited to, 4x and 20x, respectively.

[0024] In some embodiments, the second depth of field is smaller than the first depth of field. For purposes of illustration only, the first depth of field and the second depth of field may be, but are not limited to, approximately 50 micrometers and approximately 1 micrometer, respectively. In some embodiments, the second depth of field refers to the distance between the closest, sharp, and focused object relative to the second objective lens 410 and the farthest, sharp, and focused object relative to the second objective lens 410 in the second field of view. Examples of distances may be distance 21, distance 22, distance 23, or distance 24 of distance 2 shown in FIG. 4A . Distance 2 shown in FIG. 4A corresponds to the same distance 2 shown in FIGS. 2 and 3A .

[0025] In some embodiments, the second depth of field may correspond to a sub-layer of a layer of the cytology specimen 210 (e.g., the second layer defined by distance 2 shown in FIG. 2 ). Thus, distance 21 may correspond to a first sub-layer of the second layer of the cytology specimen 210, distance 22 may correspond to a second sub-layer of the second layer of the cytology specimen 210, distance 23 may correspond to a third sub-layer of the second layer of the cytology specimen 210, and distance 24 may correspond to a fourth sub-layer of the second layer of the cytology specimen 210. For purposes of illustration only, four sub-layers are described; however, more or fewer sub-layers may be present, depending on the second depth of field of the second objective lens 410.

[0026] In some embodiments, the second objective lens 410 is configured to acquire an image related to the sub-portion 420. More specifically, the second objective lens 410 is configured to acquire an image of a second sub-portion 421, 422, 423, and / or 424 of the sub-portion 420. In some embodiments, the second objective lens 410 is configured to focus on the second sub-portion 421, 422, 423, and / or 424 at a second depth of field.

[0027] In some embodiments, the focus may be based on a determination by an artificial intelligence engine. The artificial intelligence engine may have machine learning capabilities. For example, the artificial intelligence engine may be trained based on sample images of target cells (e.g., epithelial cells at risk of malignancy) having various contrasts, brightnesses, shapes, and other image characteristics. Thus, the artificial intelligence engine may identify images of the target cells and determine to focus on the identified images. For example, among images of second sub-portions 421, 422, 423, and 424 acquired through second objective lens 410, the artificial intelligence engine may identify that images of second sub-portions 422 and 423 contain images of the target cells. Based on the determination by the artificial intelligence engine, second objective lens 410 is driven to focus on second sub-portions 422 and 423, and the images of second sub-portions 422 and 423 are saved.

[0028] In another embodiment, the focus may be determined based on image characteristics of the target cells (e.g., epithelial cells at risk of malignancy). Such image characteristics may include, but are not limited to, color information associated with the target cells. For example, the nuclei of epithelial cells are stained blue by hematoxylin, while red blood cells remain red because they lack a stained nucleus. Therefore, focusing on blue areas in the image rather than red areas increases the probability of capturing an image of the target cells.

[0029] In some embodiments, the image of the target cell may include a first predetermined range in the RGB (red, green, blue) domain or the HSV (hue, saturation, value) domain. For example, the first predetermined range may include a range of R, G, and B values. Alternatively, the first predetermined range may include a range of hue values. On the other hand, the image of the non-target cell (e.g., normal epithelial cells, red blood cells, or other cells) may include a second predetermined range in the RGB or HSV domain that is different from the first predetermined range. For example, the second predetermined range may include a different range of R, G, and B values, or the second predetermined range may include a different range of hue values. For example, among the images of the second sub-portions 421, 422, 423, and 424 acquired through the second objective lens 410, the images of the second sub-portions 422 and 423 may include the first predetermined range. Thereby, the second objective lens 410 can be configured to focus on the second sub-portions 422 and 423 to store images of the second sub-portions 422 and 423, and not to focus on the second sub-portions 421 and 424.

[0030] 4B illustrates an image 430 associated with the second sub-portion 422 acquired through the second objective lens 220 arranged according to some embodiments of the present disclosure. The image 430 includes images of a target cell 441 and non-target cells 443 and 445. The image of the target cell 441 includes a first predetermined range in the RGB or HSV domain, and the images of the non-target cells 443 and 445 include a second predetermined range in the RGB or HSV domain. The first predetermined range in the RGB or HSV domain associated with the image of the target cell 441 causes the second objective lens 410 to focus on the second sub-portion 422.

[0031] In some embodiments, the artificial intelligence engine is configured to determine whether the number of target cells in the image 430 is greater than a threshold number of target cells. In some embodiments, with reference to FIG. 2 , in response to determining a first number greater than the threshold number, the artificial intelligence engine is configured to prompt an alert indicating that the cytology specimen 210 is suspicious. For example, the alert may alert a physician that a patient associated with the cytology specimen 210 may be suffering from a disease.

[0032] In some embodiments, in response to determining that the first number is less than or equal to the threshold number, the second objective lens 410 is further configured to focus on another second sub-portion adjacent to the second sub-portion 422 (e.g., second sub-portion 423) and acquire an image associated with the second sub-portion 423.

[0033] 4C illustrates how an exemplary multiple objective module acquires images associated with multiple second sub-portions (e.g., second sub-portions 422 and 423) of a cytology specimen through a second objective, and FIG. 4D illustrates image 450 associated with second sub-portion 423 acquired through second objective 220, arranged according to some embodiments of the present disclosure. Image 450 includes an image of target cell 451. The image of target cell 451 may include a first predetermined range in the RGB domain or the HSV domain.

[0034] In some embodiments, the artificial intelligence engine is configured to determine whether the number of target cells in image 430 and image 450 is greater than a threshold number of target cells. In some embodiments, with reference to FIG. 2 , in response to determining a first number greater than the threshold number, the artificial intelligence engine is configured to prompt an alert indicating that cytology specimen 210 is suspicious. For example, the alert may alert a physician that a patient associated with cytology specimen 210 may be suffering from a disease.

[0035] 3A , in response to determining that the number of first target cells in image 430 and image 450 is less than or equal to the threshold number, second objective lens 410 is further configured to acquire one or more images related to another subportion (e.g., subportion 351 defined by distance 2 and subregion 341) that includes the target cells, the another subportion being a different subportion than subportion 420.

[0036] FIG. 4E illustrates how an exemplary multiple-objective lens module configured according to some embodiments of the present disclosure acquires an image related to a second subportion (e.g., second subportion 462) of a cytology specimen through a second objective lens. In FIG. 4E, second objective lens 410 is configured to acquire one or more images related to subportion 460. In relation to FIG. 3A, subportion 460 may correspond to subportion 351 defined by distance 2 and subregion 341. FIG. 4F illustrates image 470 related to second subportion 462 acquired through second objective lens 220 configured according to some embodiments of the present disclosure. Image 470 includes an image of target cell 471. The image of target cell 471 may include a first predetermined range in the RGB domain or the HSV domain.

[0037] 4B and 4D, the artificial intelligence engine is configured to determine whether the number of target cells in images 430, 450, and 470 is greater than a threshold number of target cells. In some embodiments, with reference to FIG. 2, in response to determining a first number greater than the threshold number, the artificial intelligence engine is configured to prompt an alert indicating that cytology specimen 210 is suspicious. For example, the alert may alert a physician that a patient associated with cytology specimen 210 may be suffering from a disease.

[0038] 4B and 4D , in some embodiments, in response to determining that the number of first target cells in images 430, 450, and 470 is less than or equal to the threshold number, second objective 410 is further configured to acquire one or more images associated with another sub-portion that includes the target cells, the another sub-portion being a different sub-portion than sub-portions 420 and 460.

[0039] The above process may be repeated until the number of first target cells exceeds the threshold number, or, if the number of first target cells does not continue to exceed the threshold number, until the second objective 410 acquires images from all sub-portions of the cytology specimen that contain target cells.

[0040] 5 illustrates an exemplary system 500 for acquiring images associated with a cytology specimen, arranged in accordance with some embodiments of the present disclosure. System 500 includes, but is not limited to, a computing device 510, a camera 520, a multi-objective lens module 530, a stage 550, and a light source 560. In some embodiments, stage 550 is configured to carry and move a glass slide. A cytology specimen 540 is distributed on the glass slide.

[0041] In some embodiments, the computing device 510 includes a processor, a memory subsystem, and a communication subsystem. The artificial intelligence engine described above may be implemented as a set of executable instructions executed by the processor and stored in the memory subsystem.

[0042] In some embodiments, the computing device 510 is configured to generate control signals and send them to the camera 520, the multiple objective lens module 530, the stage 550, and the light source 560 via the communication subsystem. For example, the computing device 510 is configured to control the light source 560 to generate light in a specific wavelength range associated with image characteristics of target cells distributed within the cytology specimen 540. For example, the specific wavelength range may correspond to color information associated with the target cells, as described above. An example of the specific wavelength range may be from about 530 nm to about 630 nm. Alternatively, another example of the specific wavelength range is from about 450 nm to about 560 nm, preferably from about 450 nm to about 530 nm.

[0043] In some embodiments, the computing device 510 is configured to control the movement of the stage 550. When the stage 550 carries the glass slide and the cytology specimen 540, the movement of the stage 550 can align a region of the cytology specimen 540 (e.g., regions A, B, C, D, E, F, G, H, I, or J shown in FIG. 2 or subregions 341, 343, or 345 shown in FIG. 3A ) and the field of view of the objective lens 533 or 535 with the optical path of the light generated by the light source 560 and shown in FIG. 5 . Thus, the light generated by the light source 560 can pass through the region and the objective lens 533 or 535 to reach the camera 520.

[0044] In some embodiments, computing device 510 is configured to control lens switching module 531 of multiple objective lens module 530 to switch between different objective lenses (e.g., objective lenses 533 and 535). In other embodiments, computing device 510 is configured to control camera 520, multiple objective lens module 530, and / or stage 550 to focus objective lens 533 or objective lens 535 on a portion, sub-portion, and / or second sub-portion of cytology specimen 540. With reference to FIG. 2, objective lens 533 may correspond to first objective lens 220, and with reference to FIG. 4A, objective lens 555 may correspond to second objective lens 410.

[0045] In some embodiments, the computing device 510 is configured to control the camera 520 to acquire images of an in-focus portion of the cytology specimen 540. The computing device 510 is also configured to control the camera 520 to transmit the acquired images to the computing device 510 for further processing. Such processing includes, but is not limited to, identifying images associated with target cells or non-target cells within the cytology specimen 540, and comparing one or more images acquired through the second objective lens 535 with images acquired through the first objective lens 533 to determine whether the ratio of the number of target cells in the images acquired through the second objective lens 535 to the number of target cells in the images acquired through the first objective lens 533 is within a predetermined range.

[0046] FIG. 6 is a flowchart illustrating an exemplary process 600 for acquiring images associated with target cells distributed within a cytology specimen, arranged in accordance with some embodiments of the present disclosure. Process 600 may include one or more operations, functions, or actions performed by hardware, software, and / or firmware, as indicated by blocks 610, 620, 630, 640, 650, and / or 660. Each block is not limited to the described embodiment. The outlined steps and operations are provided for illustrative purposes only; some steps and operations may optionally be combined into fewer steps and operations or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments. While the blocks are shown in an ordered sequence, these blocks may be performed in parallel or in a different order than described herein. In some embodiments, process 600 may be applied to various scanning techniques for scanning cytology specimens using corresponding different whole slide imaging scanners. Such scanning techniques include, but are not limited to, area scanning or line scanning techniques.

[0047] The process 600 may begin at block 610, "Capture a first image associated with a first region of a cytology specimen." In some embodiments, with reference to FIGS. 2 and 5, the processor 510 is configured to control movement of the stage 550 so that a first region of the cytology specimen 210 (e.g., region H in FIG. 2) is aligned with a first field of view of the first objective lens 220 / 533. The processor 510 is configured to control the light source 560 to generate light in a specific wavelength range corresponding to color information associated with the target cells described above in the cytology specimen 210. An example of a specific wavelength range is about 530 nm to about 630 nm. Alternatively, another example of a specific wavelength range is about 450 nm to about 560 nm, preferably about 450 nm to about 530 nm. The light passes through the first region and the first objective lens 220 / 533. The processor 510 is configured to control the camera 520 to capture an image associated with the first region through the first objective lens 220 / 533.

[0048] In some embodiments, the first objective lens 220 / 533 has a first depth of field in the first field of view. The first depth of field may be less than the thickness of the cytology specimen 210. Thus, to obtain a clear and focused image associated with the entire first region, the camera 520 is configured to sequentially acquire images of different layers within the first region of the cytology specimen 210 according to the first depth of field. For example, with reference to FIG. 2 , the camera 520 is configured to sequentially acquire images of portions H1, H2, and H3. In some embodiments, the processor 510 is configured to identify target cells from the images of portions H1, H2, and H3 based on contrast, brightness, shape, and other image characteristics associated with the target cells within the cytology specimen 210.

[0049] Block 610 can be performed repeatedly for different regions of the cytology specimen (e.g., regions F, I, and J in FIG. 2). For example, processor 510 is configured to further acquire images of portions F1, F2, F3, I1, I2, I3, J1, J2, and J3 of cytology specimen 210 and identify target cells from the images of F1, F2, F3, I1, I2, I3, J1, J2, and J3 based on contrast, brightness, shape, and other image characteristics associated with target cells within cytology specimen 210.

[0050] In some embodiments, in response to identifying target cells as being predominantly dispersed in a particular layer (e.g., a second layer) within a region (e.g., regions F, H, I, and J) of the cytology specimen 210, the processor 510 is configured to acquire a first image (e.g., an image of portion H2) associated with that region (e.g., region H) of the cytology specimen. Finally, for example, the processor 510 is configured to acquire images A2, B2, C2, D2, E2, F2, G2, H2, I2, and J2 associated with regions A, B, C, D, E, F, G, H, I, and J, respectively.

[0051] Block 610 may be followed by block 620, "Acquire a second set of images associated with a first sub-region of the cytology specimen." In some embodiments, processor 510 is configured to sequentially identify images associated with a target cell from images A2, B2, C2, D2, E2, F2, G2, H2, I2, or J2. For example, with reference to FIGS. 3A and 3B, processor 510 is configured to identify image 333 associated with a target cell. As described above, sub-region 343 within region H can be identified based on the spatial relationship between image 333 and image region H' and the correspondence between image region H' and region H. Similarly, processor 510 may be configured to identify image 331 associated with a target cell and identify sub-region 341 within region H.

[0052] In some embodiments, as described above with reference to FIG. 3A , the second objective lens is configured to obtain a more detailed image of the target cell 324 in the first subregion 343. In some embodiments, with reference to FIGS. 4A and 5 , the processor 510 is configured to control movement of the stage 550 so that the first subregion of the cytology specimen (e.g., subregion 343 in FIG. 4A ) is aligned with the second field of view of the second objective lens 410 / 535. The processor 510 is configured to control the light source 560 to generate light in a specific wavelength range corresponding to the color information about the target cell in the cytology specimen described above. An example of a specific wavelength range is about 530 nm to about 630 nm. Alternatively, another example of a specific wavelength range is about 450 nm to about 560 nm, preferably about 450 nm to about 530 nm. The light passes through the first subregion and the second objective lens 410 / 535. Processor 510 is configured to control camera 520 to acquire a second set of images related to the first sub-region through second objective lens 410 / 535. The second set of images may include one or more images of second sub-portions 421, 422, 423, and 424 of sub-portion 420.

[0053] 4A, 4B, 4C, 4D, and 5, processor 510 is configured to control and focus second objective lens 410 / 535 to acquire a second set of images in second sub-portions 422 and 423 based on image characteristics of the target cells, e.g., color information associated with the target cells. For example, the second set of images (e.g., images 430 and 450) include color information associated with the target cells, while images associated with first sub-region 343 other than the second set of images (e.g., images in second sub-portions 421 and 424) do not include color information associated with the target cells.

[0054] Block 620 may be followed by block 630, "Identify First Target Cell Count." In some embodiments, a first target cell count is identified in the image associated with first sub-region 343. More specifically, the target cell count in images 430 and 450 is identified as the first target cell count in block 630.

[0055] Block 630 may be followed by block 640, "Determine a First Number of Target Cells Greater than a Threshold Number." In response to determining that the first number of target cells identified in block 630 is greater than the threshold number of target cells, block 640 is followed by block 650, "Prompt a Warning." In some embodiments, with reference to FIG. 2 , block 650 displays a warning indicating that the cytology specimen 210 is suspicious. On the other hand, if the first number of target cells identified in block 630 is determined to be equal to or less than the target cell threshold value, block 640 is followed by block 660, "Acquire a Third Set of Images Associated with a Second Sub-Region of the Cytology Specimen."

[0056] In block 660, in some embodiments, the second objective lens is configured to acquire a more detailed image of the target cell 324 in the second subregion 341, as described above with reference to FIGS. 3A, 4E, and 4F. In some embodiments, with reference to FIGS. 4A and 5, the processor 510 is configured to control movement of the stage 550 so that the second subregion of the cytology specimen (e.g., subregion 341 in FIG. 4E) is aligned with the second field of view of the second objective lens 410 / 535. The processor 510 is configured to control the light source 560 to generate light in a specific wavelength range corresponding to the color information about the target cell in the cytology specimen described above. An example of a specific wavelength range is about 530 nm to about 630 nm. Alternatively, another example of a specific wavelength range is about 450 nm to about 560 nm, preferably about 450 nm to about 530 nm. The light passes through the second subregion and the second objective lens 410 / 535. The processor 510 is configured to control and focus the second objective lens 410 / 535 to acquire a third set of images of the second sub-region 462 based on image characteristics of the target cells, e.g., color information associated with the target cells. Further, in some embodiments, a second number of target cells in the images associated with the second sub-region 341 is identified. More specifically, the number of target cells in the images 470 is identified as the second number of target cells in block 660.

[0057] In some embodiments, processor 510 is configured to determine whether the sum of the first number of target cells associated with first sub-region 343 and the second number of target cells associated with second sub-region 341 is greater than a threshold number of target cells specified in block 640. In response to determining that the sum is greater than the threshold number, with reference to FIG. 2 , processor 510 is configured to prompt a warning indicating that cytology specimen 210 is suspect.

[0058] In some embodiments, in response to determining that the sum is equal to or less than the threshold number, the processor 510 is configured to control and focus the second objective lens 410 / 535 to acquire an image associated with another subregion based on image characteristics of the target cells, e.g., color information associated with the target cells. With reference to FIG. 3A , the another subregion may be a subregion of the first region H. Alternatively, with reference to FIG. 2 , the another subregion may be a subregion of another region (e.g., region A, B, C, D, E, F, G, H, I, or J). The sum of target cells acquired from various subregions through the second objective lens is continuously compared to a threshold number of target cells specified in block 640. If the sum exceeds the threshold number, with reference to FIG. 5 , the processor 510 is configured to issue a warning and the process 600 ends. If the sum does not exceed the threshold number, with reference to FIG. 5 , the processor 510 controls the second objective lens 535 to repeatedly execute the process 600 until images of all subregions on the cytology specimen associated with the target cells have been acquired.

[0059] FIG. 7 is a flowchart illustrating an exemplary process 700 for acquiring images associated with target cells distributed within a cytology specimen, arranged in accordance with some embodiments of the present disclosure. Process 700 may include one or more operations, functions, or actions performed by hardware, software, and / or firmware, as indicated by blocks 710, 720, 730, and / or 740. Each block is not limited to the described embodiment. The outlined steps and operations are provided as examples only, and some steps and operations may optionally be combined into fewer steps and operations or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments. While the blocks are shown in an ordered sequence, these blocks may be performed in parallel or in a different order than described herein. In some embodiments, process 700 may be applied to various scanning techniques for scanning cytology specimens using different corresponding whole slide imaging scanners. Such scanning techniques include, but are not limited to, area scanning or line scanning techniques.

[0060] Process 700 may begin at block 710, "Acquire one or more first images associated with a first region of a cytology specimen." In some embodiments, with reference to FIGS. 2 and 5, processor 510 is configured to control movement of stage 550 so that a first region of cytology specimen 210 (e.g., region H in FIG. 2) is aligned with a first field of view of first objective lens 220 / 533. Processor 510 is configured to control light source 560 to generate light in a specific wavelength range corresponding to color information associated with target cells described above within cytology specimen 210. An example of a specific wavelength range is about 530 nm to about 630 nm. Alternatively, another example of a specific wavelength range is about 450 nm to about 560 nm, preferably about 450 nm to about 530 nm. Light passes through the first region and first objective lens 220 / 533. The processor 510 is configured to control the camera 520 to acquire an image relating to the first region through the first objective lens 220 / 533.

[0061] In some embodiments, the first objective lens 220 / 533 has a first depth of field in the first field of view. The first depth of field may be less than the thickness of the cytology specimen 210. Thus, to obtain a clear and focused image associated with the entire first region, the camera 520 is configured to sequentially acquire images of different layers within the first region of the cytology specimen 210 according to the first depth of field. For example, with reference to FIG. 2 , the camera 520 is configured to sequentially acquire images of portions H1, H2, and H3. In some embodiments, the processor 510 is configured to identify target cells from the images of portions H1, H2, and H3 based on contrast, brightness, shape, and other image characteristics associated with the target cells within the cytology specimen 210.

[0062] Block 710 may be followed by block 720, "Acquire additional images relating to other regions of the cytology specimen." In some embodiments, in block 720, the operations performed in block 710 may be repeated for different regions of the cytology specimen (e.g., regions F, I, and J in FIG. 2). For example, processor 510 may be configured to acquire additional images of portions F1, F2, F3, I1, I2, I3, J1, J2, and J3 of cytology specimen 210 and identify target cells from the images of F1, F2, F3, I1, I2, I3, J1, J2, and J3 based on contrast, brightness, shape, and other image characteristics associated with target cells within cytology specimen 210.

[0063] Block 720 may be followed by block 730, "Identify a first number of target cells and determine if the first number of target cells is greater than a threshold number." In some embodiments, the first number of target cells is identified from the first image acquired in block 710 and a further image acquired in block 720. In some embodiments, the first number of target cells may be greater than a threshold number of target cells. If the target cells are epithelial cells at risk of malignancy and the threshold number is a threshold number used by physicians to diagnose cancer, process 700 may conclude that the patient providing the cytology specimen is likely to be a cancer patient. Note with reference to FIG. 5 that the second objective lens 535 need not be used to reach this conclusion. In block 730, with reference to FIGS. 2 and 5, processor 510 is configured to prompt a warning indicating that cytology specimen 210 is suspicious.

[0064] Block 730 may be followed by block 740, "Acquire a second set of images relating to a sub-region of the cytology specimen." If the number of first target cells identified in block 730 is greater than a threshold number, and therefore the patient is highly likely to be a cancer patient, then, with reference to FIG. 5, images acquired through the second objective lens 535 can be utilized to examine the size and / or morphology of several of the most important target cells in the cytology specimen. Thus, in some embodiments, with reference to FIG. 5, processor 510 is configured to control movement of stage 550 such that second objective lens 535 acquires one image from each sub-region of the cytology specimen, thereby reducing the scanning time of the cytology specimen and acquiring images of the most important target cells in the cytology specimen.

[0065] The above examples may be implemented by hardware (including hardware logic circuitry), software, firmware, or a combination thereof. The above examples may be implemented by any suitable computing device, computer system, wearable, etc. The computing device includes a processor, a memory unit, and physical NIC(s), which may communicate with each other via a communication bus or the like. The computing device may include a non-transitory computer-readable medium having stored thereon instructions or program code that, when executed by the processor, causes the processor to perform the processes described herein with reference to FIGS. 6 and 7. The computing device may be in communication with the wearable and / or one or more sensors.

[0066] The techniques presented above may be implemented with special-purpose hardwired circuitry, software and / or firmware in combination with programmable circuitry, or a combination thereof. The special-purpose hardwired circuitry may be in the form of, for example, one or more application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc. The term "processor" shall be interpreted broadly to include processing units, ASICs, logic units, programmable gate arrays, etc.

[0067] Certain aspects of the embodiments disclosed herein may be equivalently implemented, in whole or in part, as one or more computer programs running on one or more computers (e.g., one or more programs running on one or more computing systems), as one or more programs running on one or more processors (e.g., one or more programs running on one or more microprocessors), firmware, or substantially any combination thereof, on integrated circuits. Additionally, circuit design and / or software and / or firmware code may be created in light of this disclosure.

[0068] Software and / or other instructions for implementing the techniques introduced herein can be stored on a non-transitory computer-readable medium and executed by one or more general-purpose or special-purpose programmable microprocessors. As used herein, the term "computer-readable medium" includes any mechanism that provides (i.e., stores and / or transmits) information in a form accessible by a machine (such as a computer, a network device, a personal digital assistant (PDA), a mobile device, a manufacturing tool, or any device with a set of one or more processors). Computer-readable media include recordable and non-recordable media, such as read-only memory (ROM), random-access memory (RAM), magnetic disks or optical storage media, flash memory devices, etc.

[0069] From the foregoing, it will be understood that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications are possible without departing from the scope and spirit of the present disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting.

Claims

1. 1. A method for identifying a suspicious cytology specimen containing a target cell, comprising: acquiring a first image associated with a first region of the cytology specimen through a first objective lens having a first field of view, a first magnification, and a first depth of field in the first field of view; acquiring a second set of one or more images associated with a first sub-region of the first region through a second objective lens having a second field of view, a second magnification, and a second depth of field in the second field of view; identifying a number of first target cells distributed within the space of the cytology specimen; determining whether the first number of target cells is greater than a threshold number of target cells; In response to determining that the first number of target cells is greater than a threshold number of target cells, prompting a warning indicating that the cytology specimen is suspect; In response to determining that the first number of target cells is equal to or less than a threshold number of target cells, acquiring a third set of one or more images associated with a second sub-region of the first region through the second objective lens to identify a second number of target cells distributed within a second space defined by the second sub-region and the first depth of field; Including, method.

2. the volume is defined by the first subregion and the first depth of field, and the number of first target cells distributed within the volume is identified from the second set of one or more images. The method of claim 1.

3. the first region corresponds to the first field of view; The method of claim 2.

4. the second field of view corresponds to one or more portions of the first sub-region; The method of claim 2.

5. the second magnification is greater than the first magnification, and the second depth of field is less than the first depth of field; The method of claim 2.

6. Before acquiring the first image, acquiring an image relating to another region of the cytology specimen through the first objective; determining a first layer of the cytological specimen containing the target cell corresponding to the first depth of field based on an image related to the other region; acquiring images of the first layer through the first objective lens, one of the images of the first layer being the first image; Further comprising: The method of claim 2.

7. the image characteristics associated with the target cells include contrast, brightness, shape, and color information associated with the target cells; The method of claim 2.

8. the color information corresponds to light having a first wavelength range of about 530 nm to about 630 nm, a second wavelength range of about 450 nm to about 560 nm, or a third wavelength range of about 450 nm to about 530 nm; The method of claim 7.

9. the color information includes a range of R, G, B values ​​in the RGB (red, green, blue) domain, or a range of hue values ​​in the HSV (hue, saturation, value) domain; The method of claim 7.

10. further comprising determining whether the sum of the first target cell number and the second target cell number is greater than the threshold number. The method of claim 2.

11. prompting the warning in response to determining that the sum is greater than the threshold number; In response to determining that the sum is less than or equal to the threshold number, acquiring a fourth set of one or more images associated with a third sub-region of the first region through the second objective lens or acquiring a fifth set of one or more images associated with a fourth sub-region of the second region of the cytology specimen. The method of claim 10.

12. Obtaining the second set of one or more images includes: driving the second objective lens to focus on a first volume of the cytology specimen defined by the first sub-region and the second depth of field, and acquiring an image of the first volume, the image being one of the second set of one or more images; the first space is associated with a second layer of the cytology specimen; The method of claim 2.

13. acquiring additional images relating to other regions of the cytology specimen through the first objective lens before acquiring the second set of one or more images; identifying the first target cell count before acquiring the second set of one or more images and after acquiring the further images; The method of claim 1.

14. the number of first target cells distributed in the space is identified from the first image and the further image; the space is defined by the first region, the first depth of field, the other region, and the first depth of field; The method of claim 13.

15. acquiring the second set of one or more images further comprises acquiring one single image associated with the first sub-region.

15. The method of claim 14.

16. the prompting occurs before the second set of one or more images is acquired. The method of claim 13.

17. 1. A non-transitory computer-readable medium for identifying a suspicious cytology specimen, comprising:

17. A method for implementing a method of claim 1, further comprising: storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations according to the method of any one of claims 1 to 16; Non-transitory computer-readable medium.

18. 1. A system for identifying suspicious cytology specimens, comprising: one or more processors; a non-transitory computer-readable medium coupled to one or more of the processors and having stored thereon instructions that, in response to execution by one or more of the processors, cause the one or more processors to perform operations in accordance with the method of any one of claims 1 to 16; Equipped with system.

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