11 citations · 23 across the 13 of their papers we have counts for
7 papers · 1 filter
Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data
Etienne Chollet, Yaël Balbastre, Chiara Mauri +3
Microvascular anatomy is known to be involved in various neurological disorders. However, understanding these disorders is hindered by the lack of imaging modalities capable of cap…
A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data
Etienne Chollet, Yael Balbastre, Caroline Magnain +2
Serial sectioning Optical Coherence Tomography (sOCT) is a high-throughput, label free microscopic imaging technique that is becoming increasingly popular to study post-mortem neur…
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Sean I. Young, Yaël Balbastre, Bruce Fischl +2
In magnetic resonance imaging (MRI), slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices…
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
Matthew G. French, Gonzalo D. Maso Talou, Thiranja P. Babarenda Gamage +6
In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment. Wh…
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre +6
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently hav…
A Tool for Super-Resolving Multimodal Clinical MRI
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This var…
Empirical Bayesian Mixture Models for Medical Image Translation
Mikael Brudfors, John Ashburner, Parashkev Nachev +1
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpre…