# Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network

arXiv: https://arxiv.org/abs/1911.11209 · HTML: https://mlsanity.com/paper/1911.11209/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation](https://mlsanity.com/paper/2211.15486/) (2022)
- [SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization](https://mlsanity.com/paper/2205.04329/) (2022)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice](https://mlsanity.com/paper/2412.03576/) (2024)
- [BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision](https://mlsanity.com/paper/2310.07060/) (2023)
- [Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers](https://mlsanity.com/paper/2406.06017/) (2024)
- [SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions](https://mlsanity.com/paper/2510.26961/) (2025)

## Abstract (arXiv)

In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a public dataset were retrospectively analyzed by 3D deep convolutional segmentation models with residual learning, using a novel zoom-in&out strategy. Dice similarity coefficient (DSC), Average symmetric surface distance (ASSD), and Hausdorff distance (HD) of the identified lesions were measured by using the manual tracing of lesions as the reference standard. Bootstrapping was employed for all metrics to estimate 95% confidence intervals. The models were assessed on the test set of 31 scans. The average DSC was 0.64 (0.51-0.76) with a median of 0.78. ASSD and HD were 3.6 mm (1.7-6.2 mm) and 20.4 mm (10.0-33.3 mm), respectively. To the best of our knowledge, this performance is the highest achieved on this public dataset. The latest deep learning architecture and techniques were applied for 3D segmentation on MRI scans and demonstrated to be effective for volumetric segmentation of chronic ischemic stroke lesions.