1.2k citations · 1.7k across the 23 of their papers we have counts for
25 papers
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…
Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning
Ralph Saber, David Henault, Rolando Rebolledo +2
Colorectal cancer liver metastases (CLM) are the most common type of distant metastases originating from the abdomen and are characterized by a high recurrence rate after curative…
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Malo Alefsen de Boisredon d'Assier, Eugene Vorontsov, Samuel Kadoury
Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different i…
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…
3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications
Hongliang Li, Tal Mezheritsky, Liset Vazquez Romaguera +1
Ultrasound (US) speckles are granular patterns which can impede image post-processing tasks, such as image segmentation and registration. Conventional filtering approaches are comm…
Spatiotemporal motion prediction in free-breathing liver scans via a recurrent multi-scale encoder decoder
Liset Vázquez Romaguera, Rosalie Plantefève, Samuel Kadoury
In this work we propose a multi-scale recurrent encoder-decoder architecture to predict the breathing induced organ deformation in future frames. The model was trained end-to-end f…
Predictive Model for Assessment of Pathological Response of Colorectal Liver Metastases to Chemotherapy from CT Images
Francisco Perdigon Romero, Emmanuel Montagnon, Milena Cerny +9
problem with results session, numbers are incorrect, theoretical thinking is no longer valid
Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge
Yiming Xiao, Hassan Rivaz, Matthieu Chabanas +13
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets…
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,…
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Francisco Perdigon Romero, An Tang, Samuel Kadoury
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis…
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…
UV Exposed Optical Fibers with Frequency Domain Reflectometry for Device Tracking in Intra-Arterial Procedures
Francois Parent, Maxime Gerard, Raman Kashyap +1
Shape tracking of medical devices using strain sensing properties in optical fibers has seen increased attention in recent years. In this paper, we propose a novel guidance system…
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…
On orthogonality and learning recurrent networks with long term dependencies
Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury +1
It is well known that it is challenging to train deep neural networks and recurrent neural networks for tasks that exhibit long term dependencies. The vanishing or exploding gradie…
3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds
Samuel Kadoury, William Mandel, Marjolaine Roy-Beaudry +2
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in ma…
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-…