Fully and weakly supervised graph networks for histopathology image segmentation
Objective Computer-assisted techniques and histopathology image processing technologies have significantly facilitated pathological diagnoses.Among them,histopathology image segmentation is an integral component of histopathol-ogy image processing,which generally refers to the separation of target regions(e.g.,tumor cells,glands,and cancer nests)from the background,is further used for downstream tasks(e.g.,cancer grading and survival prediction).In recent years,the rapid development of deep learning has resulted in significant breakthroughs in histopathology image seg-mentation.Segmentation networks,such as FCN and U-Net,have demonstrated strong capabilities in accurately delineat-ing edges.However,most existing deep learning methods rely on fully supervised learning mode,which depends on numer-ous accurately annotated digital histopathology images.Manual annotation,conducted by medical professionals with exper-tise in histopathology,is time-consuming and also introduces a high likelihood of missed diagnoses and false detections.Consequently,there is a scarcity of histopathology images with precise annotations.Moreover,histopathology images are highly complex,making it extremely challenging to distinguish targets from the background,thereby leading to inter-class homogeneity.Within the same dataset of tissue samples,there are significant variations among pathological objects,exhib-iting intra-class heterogeneity.Differences between patients and nonlinear relationships between image features impose high requirements on the robustness and generalization of histopathological tissue segmentation algorithms.Therefore,this study proposes a graph-based framework for histopathology image segmentation.Method The framework consists of two modes,namely,fully supervised graph network(FSGNet)and weakly supervised graph network(WSGNet),aiming to adapt to datasets with different levels of annotation and precision requirements in various application scenarios.FSGNet is used when working with samples having pixel-level labels and requiring high accuracy.It is trained in a fully supervised manner.Meanwhile,WSGNet is utilized when dealing with samples that only have sparse point labels.It utilizes weakly supervised learning to extract histopathology image information and train the segmentation network.Furthermore,the pro-posed framework uses graph convolutional networks(GCN)to represent the irregular morphology of histopathological tis-sues.GCN is capable of handling data with arbitrary structures and learns the nonlinear structure of images by constructing a topological graph based on histopathology images.This approach contributes to improving the accuracy of histopathology image segmentation.The current study introduces graph Laplacian regularization to facilitate the learning of similar fea-tures from neighboring nodes,effectively aggregating similar nodes and enhancing the proposed model's generalization capability.FSGNet consists of a backbone network and GCN.The backbone network follows an encoder-decoder structure to extract deep features from histopathology images.GCN is used to learn the nonlinear structure of histopathological tis-sues,enhancing the network's expressive power and generalization ability,ultimately resulting in the segmentation of tar-get regions from the background.WSGNet utilizes simple linear iterative clustering(SLIC)for superpixel segmentation of the original image.This method transforms the weakly supervised semantic segmentation problem into a binary classifica-tion problem for superpixels.WSGNet leverage local spatial similarity to reduce the computational complexity of subse-quent processing.In the preprocessing stage,the semantic information of point labels can be propagated to the entire super-pixel region,thereby generating superpixel labels.WSGNet is capable of accomplishing the segmentation of histopathology images even with a limited number of point annotations.Result This study paper conducted tests on two public datasets,namely,Gland Segmentation Challenge Dataset(GlaS)and Colorectal Adenocarcinoma Gland(CRAG)dataset,as well as one private dataset called Lung Squamous Cell Carcinoma(LUSC).GlaS consists of 165 images,with a training-to-testing ratio of 85:80.It is stratified based on histological grades and fields of view,and the testing set is further divided into Parts A and B(60 and 20 images,respectively).CRAG comprises 213 images of colorectal adenocarcinoma,with a training-to-testing ratio of 173:40.LUSC contains 110 histopathological images,with a training-to-testing ratio of 70:40.The perfor-mance of FSGNet was compared with FCN-8,U-Net,and UNeXt.WSGNet was compared with recently proposed weakly supervised models,such as WESUP,CDWS,and SizeLoss.The two modes of the proposed framework outperformed the comparison algorithms in terms of overall accuracy(OA)and Dice index(DI)on the three datasets.FSGNet achieved an OA of 88.15％and DI of 89.64％on GlaS Part A,OA of 91.58％and DI of 91.23％on GlaS Part B,OA of 93.74％and DI of 92.58％on CRAG,and OA of 92.84％and DI of 93.20％on LUSC.WSGNet achieved an OA of 84.27％and DI of 86.15％on GlaS Part A,OA of 84.91％and DI of 83.60％on GlaS Part B,OA of 85.50％and DI of 80.17％on CRAG,and OA of 88.45％and DI of 87.89％on LUSC.Results indicate that the proposed framework exhibits robustness and gen-eralization capabilities across different datasets because its performance does not vary significantly.Conclusion The two modes of the proposed framework demonstrate excellent performance in histopathological image segmentation.Subjective segmentation results indicate that the framework is able to achieve more complete segmentation of examples and provide more accurate predictions of the central regions of the target samples.It exhibits fewer instances of missed and false detec-tions,thereby showcasing strong generalization and robustness.
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