Posts Tagged ‘fully convolutional networks’
Complete List of Challenge Submissions
Kidney Tumor Segmentation Using Dual Fully Convolutional Networks
Han, Bing
Li, Mengzhang
Ye, Yue
Kidney tumor segmentation is a hot topic in medical image analysis. In this work, we propose a concise but tricky method, dual Fully Convolutional Networks(FCN) to segment kidney and its surrounding tumor. First FCN is utilized to coarsely detect location of kidney and tumor, while second FCN is employed for refining results of first FCN. In the 2019 Kidney Tumor Segmentation Challenge(KiTS19), 160 patients’ CT scans were used for training and our best result were obtained by this two-step FCN models trained with Focal loss.
Coarse-to-fine kidney and tumor segmentation with fully convolutional networks
Shen, Chen
Wang, Chenglong
Oda, Masahiro
Mori, Kensaku
Segmentation is one of the most important tasks in medical image analysis. With the development of deep leaning, fully convolutional networks (FCNs) have become the dominant approach for this task and their extension to 3D achieved considerable improvements for automated organ segmentation in volumetric imaging data. In this paper we demonstrate a coarse-to-fine segmentation method using FCNs for Kits 2019 challenge.