CN112102332A - Cancer WSI segmentation method based on local classification neural network - Google Patents
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Abstract
The invention belongs to the technical field of medical image intelligent processing, in particular to a cancer full-view digital pathological section segmentation method based on a local classification neural network. The method can quickly mark the areas of different tissues in the whole pathological section, effectively assist doctors in diagnosis and improve the accuracy and efficiency of diagnosis.
Description
Technical Field
The invention belongs to the technical field of medical image intelligent processing, particularly relates to a pathological section segmentation method, and more particularly relates to a cancer full-view digital pathological section segmentation method based on a local classification neural network.
Background
With the development of the full-slice scanning technology, a large number of tissue slices are scanned into full-field digital pathological sections (WSI), stored in a digitized form, and widely used in cancer pathological diagnosis. Although a professional physician can perform a diagnosis by analyzing the WSI, it is difficult for the physician to focus on all the details of the WSI due to its extremely large size. Meanwhile, the factors influencing the prognosis of cancer are very many, and doctors are also difficult to analyze the relevant information of the patient prognosis through pathological sections.
By using a machine learning algorithm, a computer is applied to pathological section analysis, learning can be performed on a large number of WSIs by virtue of the computational power advantage of the computer, the WSIs are analyzed by using a model obtained by training, and a doctor is assisted in diagnosing after the result is visualized, so that rich image information contained in the WSIs is fully utilized.
The Deep Convolutional Neural Network (DCNN) is a machine learning technology, which can effectively avoid human factors and automatically learn how to extract rich representative visual features from a large amount of marked data. The technology uses a back propagation optimization algorithm, so that a machine updates internal parameters thereof and learns the mapping relation from an input image to a label. In recent years, DCNN has greatly improved the performance of tasks in computer vision.
2012, Krizhevsky et al[1]First in ImageNet[2]The image classification competition applies a deep convolutional neural network, and obtains a champion with a Top-5 error rate of 15.3%, which causes a hot tide of deep learning. 2015 Simnyan et al[3]Propose 16 andthe 19-layer neural networks VGG-16 and VGG-19 increase the parameter quantity of the networks and further improve the result of the ImageNet image classification task. 2016 He et al[4]The use of the 152-layer residual network ResNet achieves a classification effect exceeding that of human eyes.
DCNN not only performs excellently in image classification tasks, but also in some structured output tasks, such as object detection[5-7]Semantic segmentation[8,9]The same excellent effects are obtained. If the DCNN is applied to computer-aided diagnosis (CAD), doctors can be assisted to make better medical diagnosis, early discovery and early treatment can be achieved, and the treatment effect can be improved.
The invention is based on He et al[4]The proposed ResNet provides a new cancer full-visual-field digital pathological section segmentation method based on a local classification neural network, can fully combine the characteristics of training sections, extracts abundant characteristics, and simultaneously realizes the analysis of WSI.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a segmentation method of a cancer full-visual-field digital pathological section (WSI) based on a local classification neural network (DCNN), which eliminates the influence of human factors and realizes automatic segmentation of the full-visual-field digital pathological section.
The invention provides a cancer full-visual-field digital pathological section (WSI) segmentation method based on a local classification neural network, which comprises the following specific steps of:
(1) dividing pathological sections into blocks;
firstly, Otsu method is used for pathological section images according to G channel (green channel) in RGB three channels[10]Performing threshold segmentation to remove the white background to obtain a mask of the approximate position of the tissue to be analyzed; then, partitioning the whole image into non-overlapping image blocks with the length and the width of 256 pixels, and only taking the image block in the mask as an analysis object;
(2) constructing a local image classification network model, and classifying image blocks;
the local image classification network model is constructed by removing the last two rolling blocks and the full connecting layer on the basis of ResNet18 and adding a full connecting layer; classifying the cut image blocks by using the network to obtain classified output;
in the invention, the model for executing the classification task is divided into two independent parts: the first is a classifier for distinguishing tumor regions and paracancerous regions; the other is a three-classifier for distinguishing a lymph gathering area, a necrosis area and other areas; the output of the second classifier is a value, the activation function is a sigmoid function and represents the confidence coefficient of the cancer-side region, if the confidence coefficient is high, the image block belongs to the cancer-side region, otherwise, the image block belongs to the tumor region; outputting a vector with the length of 3 by a full connection layer of the three classifiers, wherein an activation function is a softmax function, finally outputting confidence coefficients of each element in the obtained 3-dimensional vector corresponding to other regions, a lymph dense region and a necrosis region in sequence, and taking a high-confidence-coefficient as a classification result;
(3) carrying out overall heat map post-processing on the pathological section;
combining the obtained classification results into two heat maps according to the coordinate positions of the image blocks in the pathological section, wherein the two heat maps respectively correspond to the results of the two classifiers and the results of the three classifiers; the two heat maps are subjected to median filtering respectively, and then blocks with small areas in the heat maps are removed, wherein the size and the area threshold of the filter are determined according to requirements and actual conditions.
In the invention, the training process of the classification network model is as follows:
firstly, cutting a plurality of misaligned image blocks in different areas of each pathological section according to labels, wherein the length and the width of each image block are 256 pixels, and storing the image blocks as a data set. Then, training the two classifiers and the three classifiers by using a data set formed by the image blocks respectively;
the training samples of the two classifiers at least comprise 240 pathological sections, 10 thousands of image blocks in a tumor area are cut out, and 14 thousands of image blocks in a cancer-side area are cut out; the three-classifier training sample at least comprises 80 pathological sections, 24 thousands of lymph dense areas are cut out, 18 thousands of image blocks in necrotic areas are cut out, and 38 thousands of image blocks in other areas are cut out. The image blocks are all 256 pixels in length and width.
The invention also relates to a segmentation system of the cancer full-visual-field digital pathological section based on the local classification neural network, which corresponds to the segmentation method. The system comprises 3 modules, which are respectively: the system comprises a pathological section blocking module, a local image classification network model and a pathological section global heat map post-processing module, and the operations of the three steps are correspondingly executed.
In the invention, the average time of a single classifier for processing the WSI is 734.22 seconds, and the display card used in the test is GeForce GTX 1080 Ti. The segmentation effect is shown in fig. 2 and fig. 3, the present invention can accurately segment a lymph dense region and a necrotic region, and distinguish a paracancer region and a tumor region, thereby helping a doctor to quickly locate a fine tissue region, and the distribution and the area size of each region can also help the doctor to analyze and diagnose a section.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a graph showing the effect of segmenting the lymphoid dense region and necrotic region of a pathological section.
Fig. 3 is a graph showing the effect of segmenting the paracancerous region of a pathological section.
Detailed Description
The embodiments of the present invention are described in detail below, but the scope of the present invention is not limited to the examples.
By adopting the flow framework in fig. 1, 86 pieces of pathological sections marked with a lymph dense region and a necrotic region and 241 pieces of pathological sections marked with a paracarcinoma region and a tumor region are used for training two target detection neural networks respectively to obtain an automatic detection and diagnosis model.
The specific process is as follows:
(1) before training, threshold segmentation is performed on the green channel of the pathological section using Otsu's method to distinguish the background, so as to obtain the mask of the region where the tissue is located. The WSI is divided into a plurality of non-overlapping image blocks with the length and the width of 256 pixels, and then a plurality of image blocks are randomly sampled from different areas in the mask as training samples according to manual marking. The image block obtained by sampling requires that all pixels are located in the same marked area and are not overlapped with each other;
(2) during training, the initial learning rate is set to be 0.00005, and a small batch of random gradient descent method is used for minimizing a loss function. The batch size was set to 256;
(3) before training, threshold segmentation is performed on the green channel of the pathological section using Otsu's method to distinguish the background, so as to obtain the mask of the region where the tissue is located. The WSI is divided into a plurality of non-overlapping image blocks with the length and the width of 256 pixels, and only the image blocks positioned in the mask are taken as analysis objects to be sent into a network for classification. And determining the corresponding category of each image block according to the confidence coefficient obtained by the network. The classification results of the image blocks are combined into a heat map according to the position coordinates of the image blocks in the pathological section. The heat map is median filtered and the regions of too small an area are removed to obtain the final segmentation result. The larger the filter size of the median filtering and the area threshold of the removed region are, the easier the fine and outlier region is to be removed, and the specific size and threshold are determined according to the actual requirements.
FIG. 2 is a graph showing the effect of the present invention on dividing the lymph node region and the necrotic region on a pathological section. Wherein the green line range is the part predicted to be a lymph-dense region; the blue line is the part predicted to be a necrotic area.
FIG. 3 is a diagram showing the effect of segmenting the paracancerous region on a pathological section according to the present invention. Wherein the part within the range of the red line is predicted to be a paracancerous region, and the rest region except the background can be regarded as a tumor region.
Reference to the literature
[1] Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1097-1105 (2012).
[2] Russakovsky, O., Deng, J., Su, H. et al. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115, 211-252 (2015).
[3] Simonyan, K. & Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Representation Learning, (2014).
[4] He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770-778 (2016).
[5] Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition, 580-587 (2014).
[6] Girshick, R. Fast R-CNN. IEEE International Conference on Computer Vision, 1440-1448 (2015).
[7] Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. Neural Information Processing Systems, (2015).
[8] Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE International Conference on Computer Vision, 3431-3440 (2015).
[9] Chen, L., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 834-848 (2018).
[10] Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66(1979).。
Claims (2)
1. A cancer full-visual field digital pathological section segmentation method based on a local classification neural network is characterized by comprising the following specific steps:
(1) dividing pathological sections into blocks;
firstly, performing threshold segmentation on a pathological section image according to a G channel in RGB three channels by using an Otsu method to remove a white background, and obtaining a mask of the approximate position of a tissue to be analyzed; then, partitioning the whole image into non-overlapping image blocks with the length and the width of 256 pixels, and only taking the image block in the mask as an analysis object;
(2) constructing a local image classification network model, and classifying image blocks;
the local image classification network model is constructed by removing the last two rolling blocks and the full connecting layer on the basis of ResNet18 and adding a full connecting layer; classifying the cut image blocks by using the network to obtain classified output;
wherein the model that performs the classification task is divided into two separate parts: the first is a classifier for distinguishing tumor regions and paracancerous regions; the other is a three-classifier for distinguishing a lymph gathering area, a necrosis area and other areas; the output of the second classifier is a value, the activation function is a sigmoid function and represents the confidence coefficient of the cancer-side region, if the confidence coefficient is high, the image block belongs to the cancer-side region, otherwise, the image block belongs to the tumor region; outputting a vector with the length of 3 by a full connection layer of the three classifiers, wherein an activation function is a softmax function, finally outputting confidence coefficients of each element in the obtained 3-dimensional vector corresponding to other regions, a lymph dense region and a necrosis region in sequence, and taking a high-confidence-coefficient as a classification result;
(3) carrying out overall heat map post-processing on the pathological section;
combining the obtained classification results into two heat maps according to the coordinate positions of the image blocks in the pathological section, wherein the two heat maps respectively correspond to the results of the two classifiers and the results of the three classifiers; the two heat maps are subjected to median filtering respectively, and then blocks with small areas in the heat maps are removed, wherein the size and the area threshold of the filter are determined according to requirements and actual conditions.
2. The segmentation method according to claim 1, wherein the training procedure of the classification network model is as follows:
firstly, cutting a plurality of non-coincident image blocks in different areas of each pathological section according to labels, wherein the length and the width of each image block are 256 pixels, and storing the image blocks as a data set; training a two-classifier and a three-classifier respectively by using a data set formed by the image blocks;
the training samples of the two classifiers at least comprise 240 pathological sections, 10 thousands of image blocks in a tumor area are cut out, and 14 thousands of image blocks in a cancer-side area are cut out; the three-classifier training sample at least comprises 80 pathological sections, 24 thousands of lymph dense areas are cut out, 18 thousands of image blocks in necrotic areas are cut out, and 38 thousands of image blocks in other areas are cut out; the image blocks are all 256 pixels in length and width.
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