TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

2016 ◽  
Vol 43 (6Part46) ◽  
pp. 3883-3883 ◽  
Author(s):  
X Yang ◽  
A Jani ◽  
P Rossi ◽  
H Mao ◽  
W Curran ◽  
...  
Keyword(s):  
Mr Images ◽  
Prostate Segmentation

scholarly journals Automatic prostate segmentation using multiobjective active appearance modelin MR images

2019 ◽  
Vol 27 (6) ◽  
pp. 4361-4377
Author(s):  
Ahad SALIMI ◽  
Mohamad Ali POURMINA ◽  
Mohammad-Shahram MOIN
Keyword(s):  
Mr Images ◽  
Prostate Segmentation ◽  
Active Appearance

Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach

2018 ◽  
Vol 12 (8) ◽  
pp. 1629-1637 ◽  
Author(s):  
Ahad Salimi ◽  
Mohammad Ali Pourmina ◽  
Mohammad-Shahram Moin
Keyword(s):  
Active Contour ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Fully Automatic

Automatic Prostate Segmentation using Deep Learning and MR Images

2018 ◽  
Vol 102 (3) ◽  
pp. e379
Author(s):  
Y. Yuan ◽  
W. Qin ◽  
M.K. Buyyounouski ◽  
S.L. Hancock ◽  
H.P. Bagshaw ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Images ◽  
Prostate Segmentation

scholarly journals CDA-Net for Automatic Prostate Segmentation in MR Images

2020 ◽  
Vol 10 (19) ◽  
pp. 6678
Author(s):  
Zhiying Lu ◽  
Mingyue Zhao ◽  
Yong Pang
Keyword(s):  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Sequence Correlation ◽  
Correlation Processing ◽  
Mri Scans ◽  
Roi Extraction ◽  
Two Stages ◽  
Essential Prerequisite

Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.

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