5 citations · 13 across the 5 of their papers we have counts for
5 papers
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI
Bella Specktor Fadida, Bossmat Yehuda, Daphna Link Sourani +3
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain s…
Partial annotations for the segmentation of large structures with low annotation cost
Bella Specktor Fadida, Daphna Link Sourani, Liat Ben Sira Elka Miller +2
Deep learning methods have been shown to be effective for the automatic segmentation of structures and pathologies in medical imaging. However, they require large annotated dataset…
Automatic fetal fat quantification from MRI
Netanell Avisdris, Aviad Rabinowich, Daniel Fridkin +9
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive o…
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
Netanell Avisdris, Leo Joskowicz, Brian Dromey +5
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometr…