数字病理学中的计算核分割方法综述

IF 3.2 Q1 Computer Science
Vasileios Magoulianitis, Catherine A. Alexander, C.-C. Jay Kuo
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引用次数: 0

摘要

在癌症诊断过程中,数字病理学在活检组织标本上恶性区域的识别、分期和分级方面发挥着重要作用。高分辨率组织学图像的外观差异很大,这可能来自于采集设备或 H\&E 染色过程。细胞核分割是一项重要任务,因为它能检测出背景组织中的细胞核,并得出细胞核的拓扑结构、大小和数量,这些都是检测癌症的决定性因素。然而,对于病理学家来说,这是一项相当耗时的任务,而且据说主观性很强。借助现代人工智能(AI)模型的计算机辅助诊断(CAD)工具可实现细胞核分割的自动化。这可以减少分析中的主观性和阅读时间。本文从使用传统图像处理技术的早期作品开始,到遵循深度学习(DL)范式的现代方法,进行了广泛的综述。由于注释数据稀缺,我们的综述还重点关注了问题的弱监督方面。最后,我们深入讨论了不同模型和监督类型的优势。此外,我们还试图推断和展望未来的研究方向,以便在保持高性能的同时尽量减少对标注数据的需求。未来的方法应强调高效、可解释的模型以及透明的基本流程,这样医生才能信任其输出结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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