Literature DB >> 29993863

Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells.

Afaf Tareef, Yang Song, Heng Huang, Dagan Feng, Mei Chen, Yue Wang, Weidong Cai.   

Abstract

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.

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Year:  2018        PMID: 29993863     DOI: 10.1109/TMI.2018.2815013

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  7 in total

1.  Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

Authors:  Sudhir Sornapudi; Gregory T Brown; Zhiyun Xue; Rodney Long; Lisa Allen; Sameer Antani
Journal:  AMIA Annu Symp Proc       Date:  2020-03-04

2.  Local Label Point Correction for Edge Detection of Overlapping Cervical Cells.

Authors:  Jiawei Liu; Huijie Fan; Qiang Wang; Wentao Li; Yandong Tang; Danbo Wang; Mingyi Zhou; Li Chen
Journal:  Front Neuroinform       Date:  2022-05-12       Impact factor: 3.739

3.  Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm.

Authors:  Jinjie Huang; Tao Wang; Dequan Zheng; Yongjun He
Journal:  Bioengineered       Date:  2020-12       Impact factor: 3.269

Review 4.  A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.

Authors:  Teresa Conceição; Cristiana Braga; Luís Rosado; Maria João M Vasconcelos
Journal:  Int J Mol Sci       Date:  2019-10-15       Impact factor: 5.923

5.  Glass-cutting medical images via a mechanical image segmentation method based on crack propagation.

Authors:  Yaqi Huang; Ge Hu; Changjin Ji; Huahui Xiong
Journal:  Nat Commun       Date:  2020-11-09       Impact factor: 14.919

6.  A contour property based approach to segment nuclei in cervical cytology images.

Authors:  Iram Tazim Hoque; Nabil Ibtehaz; Saumitra Chakravarty; M Saifur Rahman; M Sohel Rahman
Journal:  BMC Med Imaging       Date:  2021-01-28       Impact factor: 1.930

7.  Segmentation of HE-stained meningioma pathological images based on pseudo-labels.

Authors:  Chongshu Wu; Jing Zhong; Lin Lin; Yanping Chen; Yunjing Xue; Peng Shi
Journal:  PLoS One       Date:  2022-02-04       Impact factor: 3.240

  7 in total

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