Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications-Reference-Cited by-同舟云学术

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

Published:2020-05 Issue:5 Volume:47 Page:
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Seo Hyunseok¹,Badiei Khuzani Masoud¹,Vasudevan Varun²,Huang Charles³,Ren Hongyi¹,Xiao Ruoxiu¹,Jia Xiao¹,Xing Lei¹

Affiliation:

1. Medical Physics Division in the Department of Radiation Oncology, School of Medicine Stanford University Stanford CA 94305‐5847USA

2. Institute for Computational and Mathematical Engineering, School of Engineering Stanford University Stanford CA 94305‐4042USA

3. Department of Bioengineering, School of Engineering and Medicine Stanford University Stanford CA 94305‐4245USA

Funder

National Institutes of Health

Varian Medical Systems

Google

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13649

Reference134 articles.

1. Supervised Learning-Based Cell Image Segmentation for P53 Immunohistochemistry

2. WachingerC GollandP.Atlas‐based under‐segmentation. Paper presented at: International Conference on Medical Image Computing and Computer‐Assisted Intervention2014.

3. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

4. NohH HongS HanB.Learning deconvolution network for semantic segmentation. arXiv e‐prints.2015.https://ui.adsabs.harvard.edu/\#abs/2015arXiv150504366N. Accessed May 01 2015.

5. HeK ZhangX RenS SunJ.Deep residual learning for image recognition. Paper presented at: Proceedings of the IEEE conference on computer vision and pattern recognition2016.

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