Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity d...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 38; H. 12; S. 2768 - 2778
Hauptverfasser:	Wang, Yi, Ni, Dong, Dou, Haoran, Hu, Xiaowei, Zhu, Lei, Yang, Xin, Xu, Ming, Qin, Jing, Heng, Pheng-Ann, Wang, Tianfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	3D segmentation Artificial neural networks Attention mechanisms Biomedical imaging deep features feature pyramid network Image processing Image segmentation Medical imaging Modules Neural networks Prostate Semantics Shape Three-dimensional displays transrectal ultrasound Two dimensional displays Ultrasonic imaging Ultrasound
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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