A survey on recent trends in deep learning for nucleus segmentation from histopathology images.

Anusua Basu, Pradip Senapati, Mainak Deb, Rebika Rai, Krishna Gopal Dhal
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引用次数: 0

Abstract

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017-2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.

从组织病理学图像中分割细胞核的深度学习最新趋势调查。
细胞核分割是对成像数据集进行定性研究的必要步骤,也是组织病理学图像分析中的一项复杂任务。对细胞核进行分割是癌症诊断、分期和分级的重要组成部分,但重叠的区域很难将独立的细胞核分离和区分开来。深度学习正迅速在细胞核分割领域铺平道路,其发表的大量研究文章表明了其在该领域的功效,吸引了不少研究人员。本文对过去五年(2017-2021 年)利用深度学习进行细胞核分割的情况进行了系统调查,重点介绍了各种分割模型(U-Net、SCPP-Net、Sharp U-Net 和 LiverNet),并探讨了它们的相似性、优势、使用的数据集以及正在展开的研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of the ICRP
Annals of the ICRP Medicine-Public Health, Environmental and Occupational Health
CiteScore
4.10
自引率
0.00%
发文量
3
期刊介绍: The International Commission on Radiological Protection was founded in 1928 to advance for the public benefit the science of radiological protection. The ICRP provides recommendations and guidance on protection against the risks associated with ionising radiation, from artificial sources as widely used in medicine, general industry and nuclear enterprises, and from naturally occurring sources. These reports and recommendations are published six times each year on behalf of the ICRP as the journal Annals of the ICRP. Each issue provides in-depth coverage of a specific subject area.
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