[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".
VoxelSage turns CT scans into actionable 3D insights—automatically segmenting anatomy, quantifying tumors and vessels, generating structured reports, and powering agent-guided surgical analysis.
腹部 CT 多器官分割本地 GUI 原型（React + FastAPI + nnUNetv2）— 中国生物医学工程竞赛作品
PyTorch/MONAI implementation of UNETR for 14-class 3D abdominal CT segmentation. Validation Dice: 0.8027.
Official PyTorch code for a two-stage pancreas CT segmentation pipeline: Faster R-CNN localization + TransUNet with a Dice-Hausdorff loss.
Deep learning framework for pathological CT brain segmentation using PyTorch, MONAI, transfer learning, and medical image analysis.
Deep learning model for automated CT segmentation of swallowing and chewing structures (masseters, pterygoids, larynx, pharyngeal constrictor). Prospectively validated. Research use only.
Python workflow for automatic segmentation of body, skin, bone, lungs, and airways from chest CT images in 3D Slicer.
Next.js CT segmentation viewer with SuPreM GPU inference and Cornerstone/Niivue 2D/3D medical imaging review.
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