Posts Tagged ‘V-Net’
Complete List of Challenge Submissions
KiTS challenge: VNet with attention gates and deep supervision
Tureckova, Alzbeta
Turecek, Tomas
Kominkova, Zuzana
Rodŕıguez-Sánchez, Antonio
This paper presents the 3D fully convolutional neural network extended by attention gates and deep supervision layers. The model is able to automatically segment the kidney and kidney-tumor from arterial phase abdominal computed tomography (CT) scans. It was trained on the dataset proposed by the Kidney Tumor Segmentation Challange 2019. The best solution reaches the dice score 96, 43± 1, 06 and 79, 94± 5, 33 for kidney and kidney-tumor labels, respectively. The implementation of the proposed methodology using PyTorch is publicly available at github.com/tureckova/Abdomen-CT-Image-Segmentation.
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.
Kits19TumorSegmentation with VNet
Chen, Junqiang
we design the deep learning network Vnet for segmentation tumor and kidney.Fist, preprocess the kidney and kidney tumor data,second, segmentating kidney progress split into two steps: Corse segmentation and fine segmentation.third,segmentation kidney tumor process split into two steps: 2d segmentation and 3d segmentation