# Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

arXiv: https://arxiv.org/abs/1709.09075 · HTML: https://mlsanity.com/paper/1709.09075/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation](https://mlsanity.com/paper/2204.02779/) (2022)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)
- [DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains](https://mlsanity.com/paper/2110.09473/) (2021)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks](https://mlsanity.com/paper/2108.01941/) (2021)
- [Model-based inexact graph matching on top of CNNs for semantic scene understanding](https://mlsanity.com/paper/2301.07468/) (2023)
- [AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI](https://mlsanity.com/paper/2411.02867/) (2024)
- [Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release](https://mlsanity.com/paper/2501.18716/) (2025)

## Abstract (arXiv)

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time because morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy. In order to increase the accuracy of the automated segmentation, we propose to train the network using a restricted sample selection to force the network to learn the most difficult parts of the structures. We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different available state-of-the-art methods and other recently proposed deep learning approaches. On the MICCAI 2012 dataset, our method shows an excellent performance comparable to the best challenge participant strategy, while performing significantly better than state-of-the-art techniques such as FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning approaches. Moreover, our experiments show that both the addition of the spatial priors and the restricted sampling strategy have a significant effect on the accuracy of the proposed method. In order to encourage the reproducibility and the use of the proposed method, a public version of our approach is available to download for the neuroimaging community.