medical-3

Ischemic Stroke Lesion Segmentation

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in ischemic-stroke-lesion-segmentation-7

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find ischemic-stroke-lesion-segmentation-7 models and implementations

Datasets

No datasets available.

Subtasks

No subtasks available.

Most implemented papers

Boundary loss for highly unbalanced segmentation

Christian Desrosiers, Eric Granger, Ismail Ben Ayed, J. Dolz, H. Kervadec, Jihene Bouchtiba•Wed Dec 12 2018

Inspired by graph-based optimization techniques for computing active-contour flows, a non-symmetric L2 distance on the space of contours as a regional integral is expressed, which avoids completely local differential computations involving contour points.

613

Content

0

Paper Graph

Acute and sub-acute stroke lesion segmentation from multimodal MRI

Albert Clérigues, S. Valverde, J. Bernal, J. Freixenet, A. Oliver, X. Lladó•Tue Oct 30 2018

Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance.

80 0

Paper Graph

Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

Meng-Hao Guo, Wenhui Cui, Yanling Liu, Yuxing Li, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chuyang Ye•Sun Mar 03 2019

This work proposes a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs and outperforms competing SSL-based methods on ischemic stroke lesion segmentsation.

202 0

Paper Graph

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

B. S. Manjunath, Po-Yu Kao, Jefferson W. Chen•Sun Jul 21 2019

The tractographic feature is introduced to capture potentially damaged regions and predict the modified Rankin Scale (mRS) and achieves higher accuracy than the stroke volume and the state-of-the-art feature on predicting the mRS grades of stroke patients.

5 0

Paper Graph

Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

K. Engan, Luca Tomasetti, Stine Hansen, M. Khanmohammadi, Liv Jorunn Høllesli, K. D. Kurz, Michael C. Kampffmeyer•Wed Mar 01 2023

The proposed approach leverages a novel self-supervised training mechanism that is tailored to the task of ischemic stroke lesion segmentation by exploiting color-coded parametric maps generated from Computed Tomography Perfusion scans, leading to considerable improvements in performance in the few-shot setting.

6 0

Paper Graph

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Bjoern H Menze, I. Ezhov, E. D. L. Rosa, M. Reyes, D. Robben, R. Wiest, Maria Inês Meyer, M. H. Petzsche, U. Hanning, Waldo Enrique Valenzuela Pinilla, S. Liew, F. Kofler, Alexander Hutton, Tassilo Friedrich, Teresa Zarth, Johannes Bürkle, The Anh Baran, G. Broocks, L. Meyer, C. Zimmer, T. Boeckh-Behrens, M. Berndt, B. Ikenberg, Benedikt Wiestler, J. Kirschke•Mon Jun 13 2022

An expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions with high variability in stroke lesion size, quantity and location is introduced.

193 0

Paper Graph

Adding a benchmark result helps the community track progress.