Automated ischemic stroke lesion segmentation from 3D MRI
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Abstract (arXiv)
Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. In this work, we describe our solution to ISLES 2022 segmentation task. We re-sample all images to a common resolution, use two input MRI modalities (DWI and ADC) and train SegResNet semantic segmentation network from MONAI. The final submission is an ensemble of 15 models (from 3 runs of 5-fold cross validation). Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.824), and overall rank 2 (based on the combined metric ranking).
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 6 references that are in the map. First posted on arXiv 20 Sep 2022.
Builds on
4 of 6 references.
References · 4
- 2016Identity Mappings in Deep Residual Networkscited by 1,960
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
- 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
- 2022ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation datasetcited by 104
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.