A5011203822- Cell Image Analysis Techniques
- COVID-19 diagnosis using AI
- Medical Imaging and Analysis
- Scientific Computing and Data Management
- Digital Imaging for Blood Diseases
- Radiomics and Machine Learning in Medical Imaging
- Research Data Management Practices
- Medical Imaging Techniques and Applications
Polytechnique Montréal
2021-2022
Abstract We aimed to develop and validate a deep learning model for automated segmentation histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained axon/myelin using dataset light-microscopic cross-sectional images osmium tetroxide-stained rat nerves including various axonal regeneration stages. In second dataset, accuracy determined against manual labels. Automated morphometry...
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data metadata in reusable way. First developed magnetic resonance imaging (MRI) datasets, the community-led evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, quantitative MRI (qMRI). In this work, we present an extension BIDS microscopy data, along with example datasets. Microscopy-BIDS supports common methods, including 2D/3D,
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.The includes APIs, commandline tools, documentation, tutorials.ivadomed also pre-trained such as spinal tumor segmentation vertebral labeling.Original features of include a data loader that can parse image subject metadata custom splitting or extra information during training evaluation.Any dataset following the Brain Imaging Data Structure (BIDS)...