622 citations · 708 across the 9 of their papers we have counts for
6 papers · 1 filter
ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance
Jiayu Huo, Yang Liu, Xi Ouyang +3
Accurately segmenting brain lesions in MRI scans is critical for providing patients with prognoses and neurological monitoring. However, the performance of CNN-based segmentation m…
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
Jiayu Huo, Liyun Chen, Yang Liu +4
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the…
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Fernando Pérez-García, Reuben Dorent, Michele Rizzi +9
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-th…
Enhancing Fiber Orientation Distributions using convolutional Neural Networks
Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia +4
Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from sp…
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
Fernando Pérez-García, Roman Rodionov, Ali Alim-Marvasti +3
Resective surgery may be curative for drug-resistant focal epilepsy, but only 40% to 70% of patients achieve seizure freedom after surgery. Retrospective quantitative analysis coul…
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Fernando Pérez-García, Rachel Sparks, Sébastien Ourselin
Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision. These include a lack of labels for large dataset…