Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.
semantic-segmentation
Tumor Segmentation
3260 papers • 126 benchmarks • 313 datasets
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in semantic-segmentation
Trend
Dataset
Best Model
Actions
BUS 2017 Dataset B
BUS 2017 Dataset B
DigestPath
DigestPath
LiTS17
LiTS17
Libraries
iUse these libraries to find semantic-segmentation models and implementations
3 papers 1,989
Datasets
No datasets available.
Subtasks
No subtasks available.
Most implemented papers
Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks
Wenqi Li, S. Ourselin, Tom Kamiel Magda Vercauteren, Guotai Wang•Thu Aug 31 2017
A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy.