Meeting Banner
Abstract #1302

Deep learning-based fetal brain extraction method for in utero diffusion MRI

Zexuan Zhang1, Jialong Li1, Yunwei Chen1, Yanqiu Feng1, and Xinyuan Zhang1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Synopsis

Keywords: Other AI/ML, Fetal, brain extraction

Motivation: As a critical step in diffusion MRI post-processing, fetal brain extraction remains challenging due to highly anisotropic resolution and age-related brain volume changes in in utero dMRI.

Goal(s): We aimed to develop a deep learning-based model to accurately extract the fetal brain from in utero dMRI images.

Approach: We introduced the Multi-Scale-Anisotropy Network (MSA-Net), which incorporates convolutional blocks of different dimensions to create an asymmetric receptive field and combines features from adjacent layers to capture multiscale information. MSA-Net was evaluated on two datasets with different resolutions.

Results: Results show that MSA-Net achieves accurate fetal brain extraction (mean Dice > 0.95) with strong generalization.

Impact: The proposed method can significantly streamline the tedious annotation process and improve segmentation accuracy, contributing to a fast and accurate post-processing pipeline.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords