# Automatic segmentation of MR brain images with a convolutional neural network

arXiv: https://arxiv.org/abs/1704.03295 · HTML: https://mlsanity.com/paper/1704.03295/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 26 of 26)

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- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation](https://mlsanity.com/paper/2112.13559/) (2021)
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- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
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- [Beyond one-hot encoding? Journey into compact encoding for large multi-class segmentation](https://mlsanity.com/paper/2510.00667/) (2025)
- [Predicting before Reconstruction: A generative prior framework for MRI acceleration](https://mlsanity.com/paper/2510.19472/) (2025)
- [An Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation](https://mlsanity.com/paper/2602.04227/) (2026)
- [A Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation](https://mlsanity.com/paper/2603.18042/) (2026)

## Abstract (arXiv)

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.