# Automated ischemic stroke lesion segmentation from 3D MRI

Source: [Automated ischemic stroke lesion segmentation from 3D MRI](<https://www.sota2.com/research/paper/68fa6d6f1e209131cea39d4e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:34:23.044Z

Authors: Md Mahfuzur Rahman Siddique, Dong Yang, Yufan He, Daguang Xu, Andriy Myronenko

Published: 2022-09-20T08:21:57.000Z

## Abstract

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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.09546>)

[PDF](<https://arxiv.org/pdf/2209.09546.pdf>)