Fast and robust segmentation of white blood cell images by self-supervised learning

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning appr...

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Main Authors: ZHENG, Xin, WANG, Yong, WANG, Guoyou, LIU, Jianguo
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/5884
https://ink.library.smu.edu.sg/context/sis_research/article/6885/viewcontent/miron_2018___pv.pdf
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spelling sg-smu-ink.sis_research-68852021-03-29T02:04:31Z Fast and robust segmentation of white blood cell images by self-supervised learning ZHENG, Xin WANG, Yong WANG, Guoyou LIU, Jianguo A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. 2018-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5884 https://ink.library.smu.edu.sg/context/sis_research/article/6885/viewcontent/miron_2018___pv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Cell segmentation Self-supervised learning Support vector machine White blood cell Automatic labeling of training data Health Information Technology Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Cell segmentation
Self-supervised learning
Support vector machine
White blood cell
Automatic labeling of training data
Health Information Technology
Software Engineering
spellingShingle Cell segmentation
Self-supervised learning
Support vector machine
White blood cell
Automatic labeling of training data
Health Information Technology
Software Engineering
ZHENG, Xin
WANG, Yong
WANG, Guoyou
LIU, Jianguo
Fast and robust segmentation of white blood cell images by self-supervised learning
description A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets.
format text
author ZHENG, Xin
WANG, Yong
WANG, Guoyou
LIU, Jianguo
author_facet ZHENG, Xin
WANG, Yong
WANG, Guoyou
LIU, Jianguo
author_sort ZHENG, Xin
title Fast and robust segmentation of white blood cell images by self-supervised learning
title_short Fast and robust segmentation of white blood cell images by self-supervised learning
title_full Fast and robust segmentation of white blood cell images by self-supervised learning
title_fullStr Fast and robust segmentation of white blood cell images by self-supervised learning
title_full_unstemmed Fast and robust segmentation of white blood cell images by self-supervised learning
title_sort fast and robust segmentation of white blood cell images by self-supervised learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/5884
https://ink.library.smu.edu.sg/context/sis_research/article/6885/viewcontent/miron_2018___pv.pdf
_version_ 1770575640950472704