# CompNet: Complementary Segmentation Network for Brain MRI Extraction

arXiv: https://arxiv.org/abs/1804.00521 · HTML: https://mlsanity.com/paper/1804.00521/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

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

- [CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters](https://mlsanity.com/paper/2203.15331/) (2022)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Does Medical Imaging learn different Convolution Filters?](https://mlsanity.com/paper/2210.13799/) (2022)
- [Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation](https://mlsanity.com/paper/2102.01256/) (2021)
- [ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network](https://mlsanity.com/paper/2112.09135/) (2021)
- [An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation](https://mlsanity.com/paper/2109.00622/) (2021)
- [EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction](https://mlsanity.com/paper/2206.02837/) (2022)
- [Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration](https://mlsanity.com/paper/2309.04071/) (2023)

## Abstract (arXiv)

Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.