Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach
The results suggest that the U-net architecture is highly effective in segmenting infant brain tissues from MRI data. Future work will explore enhancements such as attention mechanisms and dual-network processing for further improving segmentation accuracy.
Authors
- Lehel Dénes-Fazakas (ORCID: https://orcid.org/0009-0003-9879-5664)
- Levente Kovács (ORCID: https://orcid.org/0000-0002-3188-0800)
- László Szilágyi (ORCID: https://orcid.org/0000-0001-6722-2642)
- György Eigner (ORCID: https://orcid.org/0000-0001-8038-2210)
Institutions
- Obuda University (HU)
- Sapientia Hungarian University of Transylvania (RO)
Publication Details
- Journal
- Sensors
- Published
- 2025-02-28
- DOI
- https://doi.org/10.3390/s25051531
- Citations
- 1
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 3.49