Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

Abstract Background This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region. Methods A total of 470 prostate...

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Bibliographic Details
Main Authors: Hideaki Hirashima, Mitsuhiro Nakamura, Pascal Baillehache, Yusuke Fujimoto, Shota Nakagawa, Yusuke Saruya, Tatsumasa Kabasawa, Takashi Mizowaki
Format: Article
Language:English
Published: BMC 2021-07-01
Series:Radiation Oncology
Subjects:
Computed tomography
Fully residual deep convolutional neural network
Segmentation accuracy
Male pelvic region
Online Access:https://doi.org/10.1186/s13014-021-01867-6

Internet

https://doi.org/10.1186/s13014-021-01867-6

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