# Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

arXiv: https://arxiv.org/abs/2205.01675 · HTML: https://mlsanity.com/paper/2205.01675/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1904.02216/) (2019)
- [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018)
- [ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time](https://mlsanity.com/paper/1805.04554/) (2018)
- [Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI](https://mlsanity.com/paper/1906.08827/) (2019)
- [Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices](https://mlsanity.com/paper/2007.00833/) (2020)
- [Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning](https://mlsanity.com/paper/1710.09338/) (2017)
- [ShuffleSeg: Real-time Semantic Segmentation Network](https://mlsanity.com/paper/1803.03816/) (2018)
- [Automatic segmentation of the intracranialvolume in fetal MR images](https://mlsanity.com/paper/1708.02282/) (2017)

## Built on by (most cited first, 3 of 3)

- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking](https://mlsanity.com/paper/2407.20198/) (2024)
- [ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI](https://mlsanity.com/paper/2606.01293/) (2026)

## Abstract (arXiv)

Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To address this critical unmet need, in this work we analyzed the speed-accuracy performance of a variety of deep neural network models, and devised a symbolically small convolutional neural network that combines spatial details at high resolution with context features extracted at lower resolutions. We used multiple branches with skip connections to maintain high accuracy while devising a parallel combination of convolution and pooling operations as an input downsampling module to further reduce inference time. We trained our model as well as eight alternative, state-of-the-art networks with manually-labeled fetal brain MRI slices and tested on two sets of normal and challenging test cases. Experimental results show that our network achieved the highest accuracy and lowest inference time among all of the compared state-of-the-art real-time segmentation methods. We achieved average Dice scores of 97.99% and 84.04% on the normal and challenging test sets, respectively, with an inference time of 3.36 milliseconds per image on an NVIDIA GeForce RTX 2080 Ti. Code, data, and the trained models are available at https://github.com/bchimagine/realtimefetalbrainsegmentation.