# Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping

arXiv: https://arxiv.org/abs/2203.04299 · HTML: https://mlsanity.com/paper/2203.04299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer](https://mlsanity.com/paper/2106.03650/) (2021)
- [Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency](https://mlsanity.com/paper/2012.07042/) (2020)
- [Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge](https://mlsanity.com/paper/2007.02096/) (2020)
- [GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation](https://mlsanity.com/paper/2109.14813/) (2021)
- [Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet](https://mlsanity.com/paper/2104.00985/) (2021)
- [PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation](https://mlsanity.com/paper/1812.07907/) (2018)
- [AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy](https://mlsanity.com/paper/2105.00381/) (2021)
- [Dispensed Transformer Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.14944/) (2021)
- [FRNET: Flattened Residual Network for Infant MRI Skull Stripping](https://mlsanity.com/paper/1904.05578/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)

## Abstract (arXiv)

Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from low generalization capability. For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters. Especially, for the lifespan datasets, the model trained on an adult dataset is not applicable to an infant dataset due to the large domain difference. To address this issue, numerous methods have been proposed, where domain adaptation based on feature alignment is the most common. Unfortunately, this method has some inherent shortcomings, which need to be retrained for each new domain and requires concurrent access to the input images of both domains. In this paper, we design a plug-and-play shape refinement (PSR) framework for multi-site and lifespan skull stripping. To deal with the domain shift between multi-site lifespan datasets, we take advantage of the brain shape prior, which is invariant to imaging parameters and ages. Experiments demonstrate that our framework can outperform the state-of-the-art methods on multi-site lifespan datasets.