Posts Tagged ‘3D convolutional neural networks’
Complete List of Challenge Submissions
Kidney Segmentation Framework using 3D CNN
Meng, Zhe
Two 3D CNN was used to detect the location of kidney and separate kidney and tumor respectively. 1 Network structure The CNN structure we used is V-net [1]. The left side of the network is divided in different stages that operate at different resolutions. Each stage comprises one to three convolutional layers. The convolutions performed in each stage use volumetric kernels having size 5 × 5 × 5 voxels. As the data proceeds through different stages along the compression path, its resolution is reduced. This is performed through convolution with 2× 2× 2 voxels wide kernels applied with stride 2 . Since the second operation extracts features by considering only non overlapping 2 × 2 × 2 volume patches, the size of the resulting feature maps is halved. 2 Kidney detection One V-net was training using patches extracted from whole CT images. In testing stage, it was used firstly detect where are the kidney. 3 Kidney and tumor segmentation Another V-net was training using patches extracted around kidneys. In testing stage, it was used to separate kidney and tumor based on previous detection. References 1. Fausto Milletari1,: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.
Automatic segmentation of kidney and kidney tumor based on 3D convolutional neural networks
Qiao, Pengchong
Zhao, Bin
Liu, Zhiyang
Kidney cancer is a huge threat to humans, and the surgery is the most common treatment. For clinicians, knowing the morphology of the kidney and kidney tumor in advance may be helpful for surgery. Automatic segmentation of kidney and kidney tumor is a promising approach for these efforts. In this paper, we proposed a based 3D convolutional neural network to segment kidney and kidney tumor using the data from the 2019 Kidney Tumor Segmentation Challenge.