arXiv 2307.058002023-07-11eess.IVcs.CV

用于改进肝细胞癌全切片图像中整个肿瘤分割的分层 Transformer 编码器

A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma

Université Paris Cité（巴黎西岱大学）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；French University Institute (IUF)（法国大学研究院）

Zhuxian Guo, Qitong Wang, Henning Müller, Themis Palpanas, Nicolas Loménie, Camille Kurtz

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AI总结：

针对肝细胞癌 WSI 全肿瘤分割难题，提出分层 Transformer 架构 HiTrans，在 4096×4096 图像块上学习全局依赖并提升分割性能。

摘要：

在数字组织病理学中，对肝细胞癌（HCC）的全切片图像（WSI）进行整个肿瘤分割具有重要作用，尤其是作为自动排除健康组织的预处理过滤器，用于组织学分子关联挖掘和其他下游组织病理学任务。由于 HCC 固有的高度异质性以及缺乏大视野下的依赖关系学习，该分割任务仍具挑战性。本文提出一种带有分层 Transformer 编码器的新型深度学习架构 HiTrans，用于学习扩展后的 4096×4096 WSI 图像块内的全局依赖关系。与最先进的全卷积神经网络（FCNN）相比，HiTrans 旨在以更大的感受野和所学习到的全局依赖关系对图像块进行编码和解码。实证评估证实，通过兼顾区域和全局依赖信息，HiTrans 带来了更优的分割性能。

英文摘要：

In digital histopathology, entire neoplasm segmentation on Whole Slide Image (WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as a preprocessing filter to automatically exclude healthy tissue, in histological molecular correlations mining and other downstream histopathological tasks. The segmentation task remains challenging due to HCC's inherent high-heterogeneity and the lack of dependency learning in large field of view. In this article, we propose a novel deep learning architecture with a hierarchical Transformer encoder, HiTrans, to learn the global dependencies within expanded 4096$\times$4096 WSI patches. HiTrans is designed to encode and decode the patches with larger reception fields and the learned global dependencies, compared to the state-of-the-art Fully Convolutional Neural networks (FCNN). Empirical evaluations verified that HiTrans leads to better segmentation performance by taking into account regional and global dependency information.

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