Publications by authors named "Mireia Alenya"
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
View Article and Find Full Text PDFMulti-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.
IEEE Trans Med Imaging
October 2024
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, limiting real-world clinical applicability and acceptance.
View Article and Find Full Text PDFArticle Synopsis
- In-utero fetal MRI is becoming a crucial method for diagnosing and analyzing the developing brain, but manually segmenting cerebral structures is slow and error-prone.
- The Fetal Tissue Annotation (FeTA) Challenge was established in 2021 to promote the creation of automatic segmentation algorithms, utilizing a dataset with seven segmented fetal brain tissue types.
- The challenge saw 20 international teams submit algorithms, primarily based on deep learning techniques like U-Nets, with one team's asymmetrical U-Net architecture significantly outperforming others, establishing a benchmark for future segmentation efforts.