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

被引：32

作者：

Basu, Anusua ^{[1
]}

Senapati, Pradip ^{[1
]}

Deb, Mainak ^{[2
]}

Rai, Rebika ^{[3
]}

Dhal, Krishna Gopal ^{[1
]}

机构：

[1] Midnapore Coll Autonomous, Dept Comp Sci & Applicat, Midnapore, West Bengal, India

[2] Wipro Technol, Pune, Maharashtra, India

[3] Sikkim Univ, Dept Comp Applicat, Gangtok, Sikkim, India

来源：

EVOLVING SYSTEMS | 2024年 / 15卷 / 01期

关键词：

Image segmentation; Nucleus segmentation; White blood cell segmentation; Histopathology image segmentation; Pathology image segmentation; Hematology image segmentation; Deep learning; Machine learning; Neural network; Deep neural network; Convolutional neural network; Cancer diagnosis; CONVOLUTIONAL NEURAL-NETWORKS; FLOW-CYTOMETRIC ANALYSIS; COMPUTATIONAL PATHOLOGY; DNA-CONTENT; CANCER; CLASSIFICATION; AGGREGATION; ANNOTATION; VALIDATION; CARCINOMA;

D O I：

10.1007/s12530-023-09491-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：203 / 248

页数：46

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