Search Results for author:
Found 10 papers, 5 papers with code
Label fusion and training methods for reliable representation of inter-rater uncertainty
Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression.
2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired.
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation.
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentation tasks, little is known about the impact of individual rater style.
Benefits of Linear Conditioning with Metadata for Image Segmentation
This metadata is usually disregarded by image segmentation methods.
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.
SoftSeg: Advantages of soft versus binary training for image segmentation
SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects.
ivadomed: A Medical Imaging Deep Learning Toolbox
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.
Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.
