Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features-Reference-Cited by-同舟云学术

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Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features

Published:2022-07-11 Issue: Volume: Page:
ISSN:
Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Zhu Nanyan¹,Liu Chen²,Forsyth Britney³,Singer Zakary S.³,Laine Andrew F.³,Danino Tal³,Guo Jia⁴

Affiliation:

1. Columbia University,Department of Biological Sciences

2. Columbia University,Department of Electrical Engineering

3. Columbia University,Department of Biomedical Engineering

4. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University,Department of Psychiatry

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871026.pdf?arnumber=9871026

Reference11 articles.

1. Red blood cell segmentation by thresholding and Canny detector

2. Tracking Biological Cells in Time-Lapse Microscopy: An Adaptive Technique Combining Motion and Topological Features

3. U-Net: deep learning for cell counting, detection, and morphometry

4. U-Net: deep learning for cell counting, detection, and morphometry

5. Quantitative Time-Lapse Fluorescence Microscopy in Single Cells

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3. A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images;IEEE Journal of Biomedical and Health Informatics;2023-08

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