# DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

arXiv: https://arxiv.org/abs/2110.09473 · HTML: https://mlsanity.com/paper/2110.09473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 74 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline](https://mlsanity.com/paper/1910.03866/) (2019)
- [QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy](https://mlsanity.com/paper/1801.04161/) (2018)
- [Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound](https://mlsanity.com/paper/1601.07014/) (2016)
- [Deep Neural Networks for Anatomical Brain Segmentation](https://mlsanity.com/paper/1502.02445/) (2015)
- [Sub-cortical brain structure segmentation using F-CNN's](https://mlsanity.com/paper/1602.02130/) (2016)
- [Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features](https://mlsanity.com/paper/1709.09075/) (2017)
- [End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation](https://mlsanity.com/paper/2002.05487/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Review of Causality for Learning Algorithms in Medical Image Analysis](https://mlsanity.com/paper/2206.05498/) (2022)
- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)

## Abstract (arXiv)

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject MRIs are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a robust and efficient deep brain segmentation solution. The method consists of a pre-processing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for independent testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and extensive testing on external datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average DSC of 0.89 ± 0.04 on the independent testing datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 42 minutes for a reference registration-based pipeline to 1 minute. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. The method is publicly available on GitHub, as well as a pip package for convenient usage.