# Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism

arXiv: https://arxiv.org/abs/2405.15205 · HTML: https://mlsanity.com/paper/2405.15205/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks](https://mlsanity.com/paper/1605.07866/) (2016)
- [Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge](https://mlsanity.com/paper/2007.02096/) (2020)
- [Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning](https://mlsanity.com/paper/1710.09338/) (2017)
- [Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation](https://mlsanity.com/paper/1712.05319/) (2017)

## Built on by (most cited first, 1 of 1)

- [ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI](https://mlsanity.com/paper/2606.01293/) (2026)

## Abstract (arXiv)

Automatic segmentation of the fetal brain is still challenging due to the health state of fetal development, motion artifacts, and variability across gestational ages, since existing methods rely on high-quality datasets of healthy fetuses. In this work, we propose a novel cascade network called CasUNext to enhance the accuracy and generalization of fetal brain MRI segmentation. CasUNext incorporates depth-wise separable convolution, attention mechanisms, and a two-step cascade architecture for efficient high-precision segmentation. The first network localizes the fetal brain region, while the second network focuses on detailed segmentation. We evaluate CasUNext on 150 fetal MRI scans between 20 to 36 weeks from two scanners made by Philips and Siemens including axial, coronal, and sagittal views, and also validated on a dataset of 50 abnormal fetuses. Results demonstrate that CasUNext achieves improved segmentation performance compared to U-Nets and other state-of-the-art approaches. It obtains an average Dice coefficient of 96.1% and mean intersection over union of 95.9% across diverse scenarios. CasUNext shows promising capabilities for handling the challenges of multi-view fetal MRI and abnormal cases, which could facilitate various quantitative analyses and apply to multi-site data.