63 citations · 216 across the 11 of their papers we have counts for
6 papers · 1 filter
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Lucas Fidon, Michael Aertsen, Florian Kofler +18
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, wi…
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation
Muhammad Asad, Lucas Fidon, Tom Vercauteren
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive…
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Lucas Fidon, Suprosanna Shit, Ivan Ezhov +3
Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizabili…
Partial supervision for the FeTA challenge 2021
Lucas Fidon, Michael Aertsen, Suprosanna Shit +4
This paper describes our method for our participation in the FeTA challenge2021 (team name: TRABIT). The performance of convolutional neural networks for medical image segmentation…
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Lucas Fidon, Michael Aertsen, Doaa Emam +9
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods…