[Applications] 13. Segmentation of Infant Brain Ventricles with Hydrocephalus Using Deep Learning-Reference-Cited by-同舟云学术

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[Applications] 13. Segmentation of Infant Brain Ventricles with Hydrocephalus Using Deep Learning

Published:2024-08-20 Issue:8 Volume:80 Page:861-866
ISSN:0369-4305
Container-title:Japanese Journal of Radiological Technology
language:en
Short-container-title:Nippon Hoshasen Gijutsu Gakkai Zasshi

Author:

Iwamoto Yutaro¹,Nonaka Masahiro²,Chen Yen-Wei³

Affiliation:

1. Engineering Informatics, Faculty of Information and Communication Engineering, Osaka Electro-Communication University

2. Department of Neurosurgery, Kansai Medical University

3. Medea Technology Course, College of Information Science and Engineering, Ritsumeikan University

Funder

Japan Society for the Promotion of Science

Publisher

Japanese Society of Radiological Technology

Link

https://www.jstage.jst.go.jp/article/jjrt/80/8/80_2024-2398/_pdf

Reference27 articles.

1. 1） Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005; 26: 839–851.

2. 2） Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 2015: 234–241.

3. 3） Buda M, Saha A, Mazurowski MA. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput Biol Med 2019; 109: 218–225.

4. 4） Ono K, Iwamoto Y, Chen YW, et al. Automatic segmentation of infant brain ventricles with hydrocephalus in MRI based on 2.5D U-Net and transfer learning. Journal of Image and Graphics 2020; 8: 42–46.

5. 5） Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv: 2102.04306, 2021.

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