A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification
A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification
复制标题
结肠镜病理学 WSI 分析的深度学习方法：准确的分割和分类
DOI：
10.1109/jbhi.2020.3040269
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发表时间：
2021-10-01
影响因子：
7.7
通讯作者：
Wu, Jian
中科院分区：
文献类型：
--
作者：
Feng, Ruiwei;Liu, Xuechen;Wu, Jian
结直肠癌（Colorectal cancer，CRC）是最严重的恶性肿瘤之一。结肠镜病理检查可以在小组织切片中识别早期结肠肿瘤细胞。但是，这样的检查是耗时的，并在高分辨率图像上耗尽。在本文中，我们提出了一个新的框架，结肠镜病理全载玻片图像（WSI）分析，包括病变分割和组织诊断。我们的框架包含一个改进的U形网络与VGG网络作为骨干，和两个计划的培训和推理，分别（培训计划和推理计划）。针对结肠镜病理学WSI的特点，在训练方案中引入了一种特定的样本选择策略和一种迁移学习策略进行模型训练。此外，我们提出了一个特定的损失函数，类的DSC损失，训练分割网络。在我们的推理方案中，我们应用基于滑动窗口的采样策略的补丁生成和二倍体集成（数据集成和模型集成）的最终预测。我们使用预测的分割掩码来生成WSI恶性的可能性的分类概率。据我们所知，DigestPath 2019是第一个挑战，也是第一个关于结肠镜组织筛查和分割的公共数据集，我们提出的框架在这个数据集上表现良好。我们的新框架在在线测试数据集上实现了0.7789的DSC和1的AUC，并在DigestPath 2019挑战赛（任务2）中赢得了2nd $的位置。我们的代码可在https://github.com/bhfs9999/colonoscopy_tissue_screen_and_segmentation上获得。
Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high resolution images. In this paper, we present a new framework for colonoscopy pathology whole slide image (WSI) analysis, including lesion segmentation and tissue diagnosis. Our framework contains an improved U-shape network with a VGG net as backbone, and two schemes for training and inference, respectively (the training scheme and inference scheme). Based on the characteristics of colonoscopy pathology WSI, we introduce a specific sampling strategy for sample selection and a transfer learning strategy for model training in our training scheme. Besides, we propose a specific loss function, class-wise DSC loss, to train the segmentation network. In our inference scheme, we apply a sliding-window based sampling strategy for patch generation and diploid ensemble (data ensemble and model ensemble) for the final prediction. We use the predicted segmentation mask to generate the classification probability for the likelihood of WSI being malignant. To our best knowledge, DigestPath 2019 is the first challenge and the first public dataset available on colonoscopy tissue screening and segmentation, and our proposed framework yields good performance on this dataset. Our new framework achieved a DSC of 0.7789 and AUC of 1 on the online test dataset, and we won the $2\text{nd}$ place in the DigestPath 2019 Challenge (task 2). Our code is available at https://github.com/bhfs9999/colonoscopy_tissue_screen_and_segmentation.