CN111950544A - Method and device for determining interest region in pathological image - Google Patents
Method and device for determining interest region in pathological image Download PDFInfo
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Abstract
The invention discloses a method and a device for determining an interested area in a pathological image, computer equipment and a computer readable storage medium. The method comprises the following steps: the pathology image is segmented to obtain a plurality of sub-regions. At least one association region for each sub-region is obtained, wherein the association region partially coincides with the sub-region. Inputting the sub-region and the associated region into a classification model to obtain a confidence that the sub-region is a region of interest. And determining the region of interest in the pathological image based on the confidence degree of the sub-region as the region of interest and a preset threshold value. According to the scheme of the invention, on one hand, the film reading efficiency and the accuracy of the film reading are improved, and on the other hand, the accuracy and the speed of determining whether the sub-region is the region of interest are also improved.
Description
Technical Field
The present invention relates to the field of medical technology, and in particular, to a method and an apparatus for determining a region of interest in a pathological image, a computer device, and a computer-readable storage medium.
Background
Currently, the diagnosis of cancer is usually confirmed by pathological examination. The tissue to be detected is sliced and then subjected to a staining operation to obtain different staining images, such as an immunohistochemical staining image. A pathologist performs a diagnosis of a pathology by observing the region of interest in the stain image, both globally and locally, under a microscope. However, when the stained image is observed manually, such as when a cancer nest is searched under a microscope, the staining method is time-consuming, labor-consuming, inefficient in image reading, and highly subjective, and misjudgment may occur.
Therefore, how to determine the region of interest in the pathological image and improve the radiograph interpretation efficiency and the radiograph interpretation accuracy becomes one of the problems to be solved at present.
Disclosure of Invention
The invention provides a method, a device, computer equipment and a computer readable storage medium for determining an interested area in a pathological image, so as to determine the interested area in the pathological image. On the one hand, the efficiency of reading the pathological images is improved, and on the other hand, the accuracy of reading the pathological images is also improved.
The invention provides a method for determining an interested area in a pathological image, which comprises the following steps:
segmenting the pathological image to obtain a plurality of sub-regions;
acquiring at least one association region of each sub-region, wherein the association region is partially overlapped with the sub-region;
inputting the sub-region and the associated region thereof into a classification model to obtain a confidence degree that the sub-region is a region of interest;
and determining the region of interest in the pathological image based on the confidence degree of the sub-region as the region of interest and a preset threshold value.
Optionally, the sub-region is included in its associated region.
Optionally, the centers of the sub-regions and their associated regions are the same.
Optionally, the shape of the sub-region and its associated region are the same.
Optionally, the number of the associated sub-regions is two.
Optionally, the region of interest is a cancer nest.
The invention also provides a device for determining the region of interest in the pathological image, comprising:
the segmentation unit is used for segmenting the pathological image to obtain a plurality of sub-regions;
the acquisition unit is used for acquiring at least one associated area of each sub-area, wherein the associated area is partially overlapped with the sub-area;
the classification model is used for inputting the sub-region and the related region thereof and outputting the confidence coefficient that the sub-region is the region of interest;
a determining unit, configured to determine a region of interest in the pathology image based on the confidence level of the sub-region for the region of interest and a preset threshold.
The invention also provides a computer device comprising at least one processor and at least one memory, wherein the memory stores a computer program which, when executed by the processor, enables the processor to carry out the above-mentioned method of determining a region of interest in a pathology image.
The invention also provides a computer readable storage medium having instructions which, when executed by a processor in a device, enable the device to perform the above-described method of determining a region of interest in a pathology image.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
segmenting a pathological image to obtain a plurality of sub-regions, and acquiring at least one associated region of each sub-region, wherein the associated region is partially overlapped with the sub-regions. Inputting the sub-region and the associated region into a classification model to obtain a confidence that the sub-region is a region of interest. And determining the region of interest in the pathological image based on the confidence degree of the sub-region as the region of interest and a preset threshold value. Because pathological images do not need to be read in a manual mode, the reading efficiency is improved to a certain extent, and the reading accuracy is also improved. Further, when judging whether the sub-region is the region of interest, not only the sub-region is input to the classification model, but also the associated region of the sub-region is input to the classification model, so that the input of the classification model integrates region information of multiple scales including the sub-region, and further improves the accuracy of classification, namely the accuracy of determining whether the sub-region is the region of interest. In addition, when the interested region in the pathological image is determined, the pathological image is segmented into a plurality of sub-regions, and the interested region in the pathological image is determined by judging whether each sub-region is the interested region, so that the speed of determining the interested region in the pathological image is improved to a certain extent. In addition, the method for determining the region of interest in the pathological image is simple and efficient.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a pathology image according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for determining a region of interest in a pathology image according to an embodiment of the present invention;
FIG. 3 is a diagram of a classification model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As mentioned in the background, the prior art searches for regions of interest, such as cancer nests (mainly composed of cancer cells), by observing pathological sections globally and locally under a microscope, however, manually searching for regions of interest is time consuming and laborious and may also present certain misjudgments. Therefore, in the embodiment of the invention, the pathological section can be scanned after being amplified by a certain multiple, such as 20 times and 40 times, so as to obtain the pathological image, and the region of interest in the pathological image is determined in an artificial intelligence manner, so that the film reading efficiency and the accuracy of the film reading are improved. Fig. 1 is a schematic diagram of a pathological image according to an embodiment of the present invention, and the pathological image in fig. 1 includes tumor cells (positive tumor cells, negative tumor cells), immune cells, other cells, and the like. Generally, the determination of whether a cell in a pathological image is a cancer cell is mainly based on the size of the stained cell nucleus and the depth of the color of the cell nucleus, and the large cell nucleus is light in color, and is usually a cancer cell. The inventors consider that when identifying cancer nests in pathological images, the relative sizes and relative colors exhibited by the cell nuclei differ when the sizes of the regions to be identified differ. For example, when the area to be identified is small, the cell nucleus is relatively represented to be large, and the staining is relatively represented to be deep, but as the area to be identified is large, the cell nucleus is relatively represented to be small, and the staining is relatively represented to be shallow. Therefore, the inventors propose that, when cancer cells are identified by a neural network, not only one sub-region to be identified is input, but a plurality of associated regions associated with the sub-region to be identified are obtained on the basis of the sub-region to be identified, and the sub-region to be identified and the associated regions thereof are input to the neural network together to determine whether the input region to be identified is a cancer nest or not by the neural network.
The technical solution of the present invention is described in detail below with the region of interest as a cancer nest, but the technical solution of the present invention can also identify other regions of interest, such as regions composed of other cells, and therefore, the region of interest as a cancer nest should not be taken as a limitation to the technical solution of the present invention.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating a method for determining a region of interest in a pathological image according to an embodiment of the present invention. As shown in fig. 2, the method for determining a region of interest in a pathology image according to an embodiment of the present invention includes:
s101: the pathology image is segmented to obtain a plurality of sub-regions.
S102: at least one association region for each sub-region is obtained, wherein the association region partially coincides with the sub-region.
S103: inputting the sub-region and the associated region into a classification model to obtain a confidence that the sub-region is a region of interest.
S104: and determining the region of interest in the pathological image based on the confidence degree of the sub-region as the region of interest and a preset threshold value.
S101 is executed to segment the pathological image to obtain a plurality of sub-regions, in this embodiment, the pathological image may be segmented into a plurality of sub-regions of 512 x 512 size, or into sub-regions larger or smaller than the pathological image, and a person skilled in the art may segment the pathological image into sub-regions of different sizes according to actual requirements. The shape of the sub-regions may be square, rectangular, circular, etc.
Executing S102: at least one associated region for each sub-region is obtained. In this embodiment, the associated region associated with the sub-region partially overlaps with the sub-region, and may be a region larger than the sub-region or a region smaller than the sub-region, and the shape of the associated region may be the same as or different from that of the sub-region, for example, the sub-region is a square, and the associated region is a rectangle. The sub-regions are rectangular and the associated regions are square. In addition, the association region and the center of the sub-region thereof may be the same or different. In order to facilitate obtaining the association region of the sub-region and improve the accuracy of classifying the sub-region in the embodiment, the association region is the same as the center of the sub-region and has the same shape. Specifically, in this embodiment, in consideration of the accuracy and the classification speed of the final classification of the sub-regions, two associated regions associated with each sub-region are selected for each sub-region. Specifically, taking the region of the sub-region 512 x as an example, the associated regions may be two associated regions of the same size as the center and shape of the sub-region 1024 x, 2048 x 2048, respectively. Of course, in other embodiments, the sub-regions may be 2048 x 2048 and the associated regions may be two associated regions of the same size as their centers and shapes, 1024 x 1024 and 512 x, respectively.
Executing S103: inputting the sub-region and the associated region into a classification model to obtain a confidence that the sub-region is a region of interest. In this embodiment, the classification model includes: the system comprises a feature extraction network and a classification network, wherein the output of the feature extraction network is used as the input of the classification network. Referring to fig. 3, fig. 3 is a schematic diagram of a classification model according to an embodiment of the present invention, which is described with reference to a region with a sub-region of 512 x 512 and regions with associated regions of 1024 x 1024 and 2048 x 2048, respectively.
As shown in fig. 3, the sub-regions and their associated regions are used as three inputs of the classification model to the feature extraction network, and for each input, the input may be passed through a plurality of consecutive convolution modules to output a corresponding feature map, in this embodiment, the size of the output feature map is 256 x 256, in other embodiments, the size of the output feature map may also be 128 x 128 or 64 x 64. The number of convolution modules can be selected by those skilled in the art according to actual requirements to output feature maps of different sizes. As shown in fig. 3, in the embodiment, the sub-region of 512 x 512 outputs the feature map with the size of 256 x 256 through one convolution module, the associated region of 1024 x 256 outputs the feature map with the size of 256 x 256 through two convolution modules, and the associated region of 2048 x 2048 outputs the feature map with the size of 256 x 256 through three convolution modules. Each convolution module may include a 3 x 3 2D convolution layer, a Batch Normalization layer (BN), an activation layer, and a 2 x 2 max pooling layer. The activation function may be a Linear rectification function (ReLU). The three 256 x 256 feature maps output by the feature network are merged into one 256 x 96 feature map and then input to the classification network to output the final classification result, i.e. the confidence that the sub-region is a cancer nest. In this embodiment, the classification network may include 2 consecutive full-connection layers, and a dropout layer with a throughput rate of 0.5 may be between the full-connection layers. The second fully-connected layer output sub-region is the confidence that the cancer nests, and by softmax operation the sum of the confidence that the sub-region is a cancer nest and the confidence that the sub-region is not a cancer nest is 1.
It should be noted that fig. 3 only shows a schematic diagram of the classification model according to the embodiment of the present invention, and those skilled in the art may select different types of classification models according to actual needs as long as classification of the sub-regions can be achieved.
And S104, determining the region of interest in the pathological image based on the confidence degree of the sub-region for the region of interest and a preset threshold value. In this embodiment, the preset threshold may be 0.5, that is, when the confidence that the sub-region output by the classification model is a cancer nest is greater than 0.5, the sub-region is a cancer nest.
In this embodiment, the sub-region determined as the cancer nest may be outlined or marked with different colors, which may be beneficial for a doctor to observe and diagnose the cancer nest. In this embodiment, when determining whether the sub-region is a cancer nest, the sub-region and the information of the associated region thereof are fused to classify the sub-region, so that the accuracy of classifying the sub-region is improved, that is, the accuracy of determining whether the sub-region is a cancer nest is improved. By adopting the mode of dividing the pathological image into the plurality of sub-regions and determining whether the plurality of sub-regions are the cancer nests one by one, the speed of determining the cancer nests in the pathological image is improved to a certain extent, and the method for determining the cancer nests in the pathological image is rapid, simple and high in accuracy.
The invention also provides a device for determining the region of interest in the pathological image, which comprises:
and the segmentation unit is used for segmenting the pathological image to obtain a plurality of sub-regions.
An obtaining unit, configured to obtain at least one associated region of each sub-region, where the associated region partially coincides with the sub-region.
And the classification model is used for inputting the sub-region and the related region thereof and outputting the confidence degree that the sub-region is the region of interest.
A determining unit, configured to determine a region of interest in the pathology image based on the confidence level of the sub-region for the region of interest and a preset threshold.
The implementation of the apparatus for determining a region of interest in a pathological image in this embodiment can refer to the implementation of the method for determining a region of interest described above, and is not described herein again.
Based on the same technical concept, embodiments of the present invention provide a computer device, comprising at least one processor and at least one memory, wherein the memory stores a computer program, which when executed by the processor, enables the processor to perform the above-mentioned method of determining a region of interest in a pathological image.
Based on the same technical concept, embodiments of the present invention provide a computer-readable storage medium, in which instructions, when executed by a processor in a device, enable the device to perform the above-described method of determining a region of interest in a pathology image.
It should be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method of determining a region of interest in a pathology image, comprising:
segmenting the pathological image to obtain a plurality of sub-regions;
acquiring at least one association region of each sub-region, wherein the association region is partially overlapped with the sub-region;
inputting the sub-region and the associated region thereof into a classification model to obtain a confidence degree that the sub-region is a region of interest;
and determining the region of interest in the pathological image based on the confidence degree of the sub-region as the region of interest and a preset threshold value.
2. The method of claim 1, wherein the sub-region is contained in its associated region.
3. The method of claim 1, wherein the centers of the sub-regions and their associated regions are the same.
4. The method of claim 1, wherein the shape of the sub-regions and their associated regions are the same.
5. The method of claim 1, wherein there are two associated regions of the sub-regions.
6. The method of claim 1, wherein the region of interest is a cancer nest.
7. An apparatus for determining a region of interest in a pathology image, comprising:
the segmentation unit is used for segmenting the pathological image to obtain a plurality of sub-regions;
the acquisition unit is used for acquiring at least one associated area of each sub-area, wherein the associated area is partially overlapped with the sub-area;
the classification model is used for inputting the sub-region and the related region thereof and outputting the confidence coefficient that the sub-region is the region of interest;
a determining unit, configured to determine a region of interest in the pathology image based on the confidence level of the sub-region for the region of interest and a preset threshold.
8. A computer device comprising at least one processor and at least one memory, wherein the memory stores a computer program that, when executed by the processor, enables the processor to perform the method of any of claims 1-6.
9. A computer readable storage medium having instructions which, when executed by a processor within a device, enable the device to perform the method of any of claims 1 to 6.
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