[MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.
CTseg: A Tool for Brain CT Segmentation, Spatial Normalisation, and Volumetrics
This is the official repository for Fast-nnUNet, a new fast model inference framework based on the nnUNet framework implementation.
The implementation of our MICCAI22 paper "Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans".
Deep learning model for automated CT segmentation of swallowing and chewing structures (masseters, pterygoids, larynx, pharyngeal constrictor). Prospectively validated. Research use only.
Next.js CT segmentation viewer with SuPreM GPU inference and Cornerstone/Niivue 2D/3D medical imaging review.
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