Abdominal CT urography kidney segmentation using spatiotemporal fully convolutional network

Wankang Zeng; Wenkang Fan; Zhuohui Zheng; Rong Chen; Song Zheng; Jianhui Chen; Rong Liu; Zengqin Liu; Yinran Chen; Xiongbiao Luo

doi:10.1117/12.2581958

15 February 2021 Abdominal CT urography kidney segmentation using spatiotemporal fully convolutional network

Wankang Zeng, Wenkang Fan, Zhuohui Zheng, Rong Chen, Song Zheng, Jianhui Chen, Rong Liu, Zengqin Liu, Yinran Chen, Xiongbiao Luo

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970L (2021) https://doi.org/10.1117/12.2581958
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Kidney segmentation is fundamental for accurate diagnosis and treatment of kidney diseases. Computed tomography urography imaging is commonly used for radiologic diagnosis of patients with urologic disease. Recently, 2D and 3D fully convolutional networks are widely employed for medical image segmentation. However, most 2D fully convolutional networks do not take inter-slice spatial information into consideration, resulting in incomplete and inaccurate segmentation of targets in 3D volumes. While the spatial information is truly important for 3D volumes segmentation. To tackle these problems, we propose a computed tomography urography kidney segmentation method on the basis of spatiotemporal fully convolutional networks that employ the convolutional long short-term memory network to model inter-slice features of computed tomography urography images. We trained and tested our proposed method on kidney computed tomography urography data. The experimental results demonstrate our proposed method can effectively leverage the inter-slice spatial information to achieve better (or comparable) results than current 2D and 3D fully convolutional networks.

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Wankang Zeng, Wenkang Fan, Zhuohui Zheng, Rong Chen, Song Zheng, Jianhui Chen, Rong Liu, Zengqin Liu, Yinran Chen, and Xiongbiao Luo "Abdominal CT urography kidney segmentation using spatiotemporal fully convolutional network", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970L (15 February 2021); https://doi.org/10.1117/12.2581958

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