A Deep Learning Approach for Liver Segmentation and Lesion Detection in Medical Images Using U-Net Segmentation Model-Reference-Cited by-同舟云学术

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A Deep Learning Approach for Liver Segmentation and Lesion Detection in Medical Images Using U-Net Segmentation Model

Published:2023-12-18 Issue: Volume: Page:116-121
ISSN:
Container-title:2023 16th International Conference on Developments in eSystems Engineering (DeSE)
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Author:

Mahida Kaushik¹,Jin Daniel²,Khan Bilal¹,Khan Wasiq³,Dajani Khalil¹,Jin Jennifer¹

Affiliation:

1. California State University San Bernardino,School of Computer Science and Engineering,San Bernardino,USA

2. Loma Linda University Medical Center,Division of Vascular & Interventional Radiology,Department of Radiology,Loma Linda,USA

3. Liverpool John MooresUniversity,School of Computer Science and Mathematics,Liverpool,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10468572/10468673/10469514.pdf?arnumber=10469514

Reference14 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

2. Liver segmentation in MRI: A fully automatic method based on stochastic partitions

3. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

4. U-Net: Convolutional Networks for Biomedical Image Segmentation;P;in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, J. and W. W. M. and F. A. F. Navab Nassir and Hornegger, Ed,2015

5. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

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