Posts Tagged ‘Coarse-to-fine Kidney Segmentation Framework’
Complete List of Challenge Submissions
Coarse to Fine Framework for Kidney Tumor Segmentation
Liu, Shuolin
Accurate segmentation of kidney tumor is a key step in image-guided radiation therapy. However, shapes, scales and appearance vary greatly from patient to patient, which pose a serious challenge to segment targets correctly. In this work, we proposed a coarse-to-fine framework to automatically segment kidney and tumor computed tomography (CT) images. Specifically, we adopt two resolutions and propose a improved 3D U-Net network for kidney tumor segmentation. The model in the coarse resolution can robustly localize the kidney, while the model in the fine resolution can accurately refine the boundary of kidney and tumor.
A Coarse-to-fine Framework for Automated Kidney and Kidney Tumor Segmentation from Volumetric CT Images
Chen, Huai
Wu, Xiyi
Huang, Yijie
Wang, Lisheng
Automatic semantic segmentation of kidney and kidney tumor is a promising tool for the treatment of kidney cancer. Due to the wide variety in kidney and kidney tumor morphology, it is still a great challenge to complete accurate segmentation of kidney and kidney tumor. We propose a new framework based on our previous work accepted by MICCAI2019, which is a coarse-to-fine segmentation framework to realize accurate and fast segmentation of kidney and kidney tumor.
Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction
Zhang, Yue
Wu, Jiong
Chen, Yifan
Wu, Ed X.
Tang, Xiaoying
In this work, we formulate this segmentation problem into two sub-task: 1) kidney segmentation 2) tumor segmentation. In the first task, three 2D CNN are used to separate the kidney(including tumor )region with background (two class segmentation). In the second task, one 3D CNN and one two channel 2D CNN are used to separate detect and refine tumor within a relative smaller image region.