1.2k citations · 1.7k across the 23 of their papers we have counts for
14 papers · 1 filter
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
Malo de Boisredon, Eugene Vorontsov, William Trung Le +1
Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This is…
End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction
Matej Gazda, Peter Drotar, Liset Vazquez Romaguera +1
Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating cancer patients. One of the biggest challenges is precise treatment delivery that accounts…
Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
William Trung Le, Chulmin Bang, Philippine Cordelle +4
Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitu…
Label noise in segmentation networks : mitigation must deal with bias
Eugene Vorontsov, Samuel Kadoury
Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are diff…
Towards annotation-efficient segmentation via image-to-image translation
Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham +2
Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use o…
End-to-End Discriminative Deep Network for Liver Lesion Classification
Francisco Perdigon Romero, Andre Diler, Gabriel Bisson-Gregoire +5
Colorectal liver metastasis is one of most aggressive liver malignancies. While the definition of lesion type based on CT images determines the diagnosis and therapeutic strategy,…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
Marc-Antoine Boucher, Sarah Lippe, Amelie Damphousse +2
Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventric…
Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis
William Mandel, Olivier Turcot, Dejan Knez +2
Anterior Vertebral Body Growth Modulation (AVBGM) is a minimally invasive surgical technique that gradually corrects spine deformities while preserving lumbar motion. However the s…
Liver lesion segmentation informed by joint liver segmentation
Eugene Vorontsov, An Tang, Chris Pal +1
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes. We build the model from two fully convolutional networks, connecte…
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov +6
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Netwo…
The Importance of Skip Connections in Biomedical Image Segmentation
Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand +2
In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long s…
Prior-based Coregistration and Cosegmentation
Mahsa Shakeri, Enzo Ferrante, Stavros Tsogkas +4
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map o…
Sub-cortical brain structure segmentation using F-CNN's
Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante +4
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-…