Ischemic Stroke Lesion Segmentation
7 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Boundary loss for highly unbalanced segmentation
We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.
Acute and sub-acute stroke lesion segmentation from multimodal MRI
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available.
Predicting Clinical Outcome of Stroke Patients with Tractographic Feature
However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.