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Proceedings Paper

Accurate segmentation of bladder wall and tumor regions in MRI using stacked dilated U-Net with focal loss
Author(s): Hong Pan; Ziqiang Li; Runqiu Cai; Yaping Zhu
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Paper Abstract

Automatic and accurate segmentation of bladder walls and tumors in magnetic resonance imaging (MRI) is a challenging task, due to significant bladder shape variations, strong intensity inhomogeneity in urine and very high variability across tumors appearance. To tackle such issues, we propose to leverage the representation capacity of an improved U-Net networks using stacked dilated convolutions. The proposed structure includes stacked dilated convolutions to increase the receptive field without incurring gridding artifacts. In addition, we embed stacked dilated convolution network into the U-Net architecture, thus enabling extracting multi-scale features for segmentation of multi structures with different shapes and scales. Finally, we apply a focal loss function to make all classes contribute equally to the loss function in our model. Evaluations on T2-weighted MRI show the proposed model achieves a higher level of accuracy than state-of-the-art methods, with a mean Dice similarity coefficient of 0.95, 0.81 and 0.66 for inner wall, outer wall and tumor region segmentation, respectively. These results demonstrate a strong agreement with reference standards and a high performance gain compared with existing methods.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 114310B (14 February 2020); doi: 10.1117/12.2538323
Show Author Affiliations
Hong Pan, Southeast Univ. (China)
Ziqiang Li, Southeast Univ. (China)
Runqiu Cai, Southeast Univ. (China)
Yaping Zhu, Communication Univ. of China (China)

Published in SPIE Proceedings Vol. 11431:
MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging
Hong Sun; Bruce Hirsch; Chao Cai, Editor(s)

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