ct-segmentation

[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.

An end-to-end AI pipeline that screens lung CT slices for nodules and localizes them, using patient risk factors, a CNN classifier, and U-Net segmentation.

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 implementation of EGPDC-Seg for federated abdominal CT multi-organ segmentation with partially labeled data.

Organ-wise analysis of pediatric fine-tuning gains in an adult-trained 7-organ CT segmentation model using TotalSegmentator V3 data.

3D bone mesh reconstruction and computational geometry pipeline from CT segmentation masks using Marching Cubes, Laplacian smoothing, and 3D Slicer validation.

TS-Portable: 8-bit integer TotalSegmentator for fast CT organ segmentation on the CPU, no GPU or Python needed (command line and desktop app)

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.

Мой первый архивный ML-проект (2022) по сегментации КТ-изображений лёгких на датасете MosMedData (МосМедДата) с честной точностью 0.44%

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.

Deep learning model for automated CT segmentation of swallowing and chewing structures (masseters, pterygoids, larynx, pharyngeal constrictor). Prospectively validated. Research use only.