869 citations · 3.1k across the 11 of their papers we have counts for
11 papers
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…
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…
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
Lucas Fidon, Michael Aertsen, Nada Mufti +13
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…
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…
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
Xiangde Luo, Guotai Wang, Tao Song +6
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Ne…
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu, Guotai Wang, Tao Song +6
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance f…
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
Guotai Wang, Michael Aertsen, Jan Deprest +3
Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutiona…
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang, Wenqi Li, Michael Aertsen +3
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segm…
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang, Wenqi Li, Maria A. Zuluaga +8
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate…
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang, Maria A. Zuluaga, Wenqi Li +8
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art…