# MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation

arXiv: https://arxiv.org/abs/2211.15486 · HTML: https://mlsanity.com/paper/2211.15486/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
- [Learning with Average Top-k Loss](https://mlsanity.com/paper/1705.08826/) (2017)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder](https://mlsanity.com/paper/2208.03203/) (2022)
- [Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network](https://mlsanity.com/paper/1911.11209/) (2019)
- [Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network](https://mlsanity.com/paper/2008.13109/) (2020)

## Built on by (most cited first, 9 of 9)

- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?](https://mlsanity.com/paper/2408.10966/) (2024)
- [Deep models for stroke segmentation: do complex architectures always perform better?](https://mlsanity.com/paper/2403.17177/) (2024)
- [Segmenting Small Stroke Lesions with Novel Labeling Strategies](https://mlsanity.com/paper/2408.02929/) (2024)
- [ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance](https://mlsanity.com/paper/2307.01220/) (2023)
- [BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision](https://mlsanity.com/paper/2310.07060/) (2023)
- [Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images](https://mlsanity.com/paper/2311.11742/) (2023)
- [Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers](https://mlsanity.com/paper/2406.06017/) (2024)
- [Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention](https://mlsanity.com/paper/2501.15423/) (2025)

## Abstract (arXiv)

Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy. Recently, automatic segmentation algorithms using deep learning techniques have been developed and achieved promising results. In this report, we present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke (ATLAS v2.0) dataset. Furthermore, we describe an effective post-processing strategy that can improve some segmentation metrics. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102. Our code and trained model weights are publicly available at https://github.com/King-HAW/ATLAS-R2-Docker-Submission.