Search Results for author:
Found 21 papers, 2 papers with code
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods.
Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects
Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection.
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method.
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
Instead, only a secured remote access to a data lake is granted to the model owner without any ability to retrieve data from the data lake.
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset
Since the standardization of Whole Slide Images (WSIs) digitization, the use of deep learning methods for the analysis of histological images has shown much potential.
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks.
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
We propose to learn a probabilistic motion model from a sequence of images.
Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning.
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution.
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images.
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion.
Learning a Probabilistic Model for Diffeomorphic Registration
Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Ranked #1 on
Diffeomorphic Medical Image Registration
on Automatic Cardiac Diagnosis Challenge (ACDC)
(using extra training data)
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+1
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart.
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.
3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex).
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS).
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes.
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Esophageal adenocarcinoma arises from Barrett's esophagus, which is the most serious complication of gastroesophageal reflux disease.
