Automatic Liver And Tumor Segmentation
3 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation
ES-UNet++ is evaluated with dataset LiTS, achieving 95. 6% for liver segmentation and 67. 4% for tumor segmentation in dice score.
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring.
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet).
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation.
