A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation
Joint Authors
Tian, Xiaohui
Shi, Zhong
Tian, Zhiqiang
Song, Jingyi
Zhang, Chenyang
Yu, Xiaofu
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Accurate segmentation ofs organs-at-risk (OARs) in computed tomography (CT) is the key to planning treatment in radiation therapy (RT).
Manually delineating OARs over hundreds of images of a typical CT scan can be time-consuming and error-prone.
Deep convolutional neural networks with specific structures like U-Net have been proven effective for medical image segmentation.
In this work, we propose an end-to-end deep neural network for multiorgan segmentation with higher accuracy and lower complexity.
Compared with several state-of-the-art methods, the proposed accuracy-complexity adjustment module (ACAM) can increase segmentation accuracy and reduce the model complexity and memory usage simultaneously.
An attention-based multiscale aggregation module (MAM) is also proposed for further improvement.
Experiment results on chest CT datasets show that the proposed network achieves competitive Dice similarity coefficient results with fewer float-point operations (FLOPs) for multiple organs, which outperforms several state-of-the-art methods.
American Psychological Association (APA)
Tian, Zhiqiang& Song, Jingyi& Zhang, Chenyang& Tian, Xiaohui& Shi, Zhong& Yu, Xiaofu. 2020. A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214975
Modern Language Association (MLA)
Tian, Zhiqiang…[et al.]. A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1214975
American Medical Association (AMA)
Tian, Zhiqiang& Song, Jingyi& Zhang, Chenyang& Tian, Xiaohui& Shi, Zhong& Yu, Xiaofu. A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214975
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214975