# Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

arXiv: https://arxiv.org/abs/1710.09338 · HTML: https://mlsanity.com/paper/1710.09338/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2017.

## Built on by (most cited first, 13 of 13)

- [Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI](https://mlsanity.com/paper/2312.03102/) (2023)
- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI](https://mlsanity.com/paper/2205.05851/) (2022)
- [Rapid head-pose detection for automated slice prescription of fetal-brain MRI](https://mlsanity.com/paper/2110.04140/) (2021)
- [Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI](https://mlsanity.com/paper/2205.01675/) (2022)
- [UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation](https://mlsanity.com/paper/2306.11490/) (2023)
- [An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis](https://mlsanity.com/paper/2205.07575/) (2022)
- [Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI](https://mlsanity.com/paper/2209.12232/) (2022)
- [Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism](https://mlsanity.com/paper/2405.15205/) (2024)
- [Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI](https://mlsanity.com/paper/2311.07234/) (2023)
- [Search Wide, Focus Deep: Automated Fetal Brain Extraction with Sparse Training Data](https://mlsanity.com/paper/2410.20532/) (2024)
- [SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction](https://mlsanity.com/paper/2505.17472/) (2025)
- [ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI](https://mlsanity.com/paper/2606.01293/) (2026)

## Abstract (arXiv)

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.