Lesion Segmentation
208 papers with code • 10 benchmarks • 13 datasets
Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
Benchmarks
These leaderboards are used to track progress in Lesion Segmentation
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Libraries
Use these libraries to find Lesion Segmentation models and implementationsDatasets
HAM10000
ISIC 2018 Task 1
DigestPath
ISIC 2017 Task 1
FGADR
University of Waterloo skin cancer database
ISIC 2018 Task 3
Most implemented papers
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools.
Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions
This report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
SahinTiryaki/Brain-tumor-segmentation-Vgg19UNet
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One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Detection-aided liver lesion segmentation using deep learning
imatge-upc/liverseg-2017-nipsws
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A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.
Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging
We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images.
Acute and sub-acute stroke lesion segmentation from multimodal MRI
NIC-VICOROB/SUNet-architecture
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Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
DSNet: Automatic Dermoscopic Skin Lesion Segmentation
We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2.
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
YH0517/CLCI_Net
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To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.
