Search Results for author:
Found 31 papers, 16 papers with code
Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis.
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness.
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.
Deep semi-supervised segmentation with weight-averaged consistency targets
Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks.
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks.
Deep Active Learning for Axon-Myelin Segmentation on Histology Data
In this paper we provide a framework for Deep Active Learning applied to a real-world scenario.
U-Net Fixed-Point Quantization for Medical Image Segmentation
We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans.
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class.
Spine intervertebral disc labeling using a fully convolutional redundant counting model
Labeling intervertebral discs is relevant as it notably enables clinicians to understand the relationship between a patient's symptoms (pain, paralysis) and the exact level of spinal cord injury.
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.
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.
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.
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.
Benefits of Linear Conditioning with Metadata for Image Segmentation
This metadata is usually disregarded by image segmentation methods.
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.
Reproducibility and Evolution of Diffusion MRI Measurements within the Cervical Spinal Cord in Multiple Sclerosis
In Multiple Sclerosis (MS), there is a large discrepancy between the clinical observations and how the pathology is exhibited on brain images, this is known as the clinical-radiological paradox (CRP).
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
The diffusion-based metrics involved are extracted from the diffusion tensor imaging and Ball-and-Stick models and quantified for every cervical vertebral level using a collection of image processing methods and an atlas-based approach.
Evaluation of distortion correction methods in diffusion MRI of the spinal cord
Besides, subjective evaluation of the quality of the correction of healthy subjects images was performed by three expert raters.
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling
To further improve the performance of the proposed method, we propose a skeleton-based search space to reduce false positive detection.
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.
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.
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.
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging.
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation.
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
(3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
Towards contrast-agnostic soft segmentation of the spinal cord
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis.
Automatic Segmentation of the Spinal Cord Nerve Rootlets
Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord.
