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15 February 2021 Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images
Xiongbiao Luo, Wankang Zeng, Wenkang Fan, Song Zheng, Jianhui Chen, Rong Liu, Zengqin Liu, Yinran Chen
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Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159619 (2021) https://doi.org/10.1117/12.2581932
Event: SPIE Medical Imaging, 2021, Online Only
Abstract
Abdominal kidney segmentation plays an essential role in diagnosis and treatment of kidney diseases, particularly in surgical planning and clinical outcome analysis before and after kidney surgery. It still remains challenging to precisely segment the kidneys from CT images. Current segmentation approaches still suffer from CT image noises and variations caused by different CT scans, kidney location discrepancy, pathological morphological diversity among patients, and partial volume artifacts. This paper proposes a fully automatic kidney segmentation method that employs a volumetric convolution driven cascaded V-Net architecture and false positive reduction to precisely extract the kidney regions. We evaluate our method on publicly available kidney CT data. The experimental results demonstrate that our proposed method is a promising method for accurate kidney segmentation, providing a dice coefficient of 0.95 better than other approaches as well less computational time.
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Xiongbiao Luo, Wankang Zeng, Wenkang Fan, Song Zheng, Jianhui Chen, Rong Liu, Zengqin Liu, and Yinran Chen "Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159619 (15 February 2021); https://doi.org/10.1117/12.2581932
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KEYWORDS
Image segmentation
Kidney
Computed tomography
Convolution
Surgery
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