Accurate segmentation of bladder wall and tumor regions in MRI using stacked dilated U-Net with focal loss

Hong Pan; Ziqiang Li; Runqiu Cai; Yaping Zhu

doi:10.1117/12.2538323

14 February 2020 Accurate segmentation of bladder wall and tumor regions in MRI using stacked dilated U-Net with focal loss

Hong Pan, Ziqiang Li, Runqiu Cai, Yaping Zhu

Author Affiliations +

Proceedings Volume 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging; 114310B (2020) https://doi.org/10.1117/12.2538323
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

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Hong Pan, Ziqiang Li, Runqiu Cai, and Yaping Zhu "Accurate segmentation of bladder wall and tumor regions in MRI using stacked dilated U-Net with focal loss", Proc. SPIE 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 114310B (14 February 2020); https://doi.org/10.1117/12.2538323

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KEYWORDS

Convolution

Image segmentation

Bladder

Tumors

Magnetic resonance imaging

Medical imaging

Performance modeling

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