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.
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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