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aics-segmentation icon aics-segmentation

Part 1 of Allen Cell and Structure Segmenter: classic image segmentation workflow

coronary-artery-tracking-via-3d-cnn-classification icon coronary-artery-tracking-via-3d-cnn-classification

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

fewshot_gan-unet3d icon fewshot_gan-unet3d

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

keras-unet-collection icon keras-unet-collection

The Tensorflow, Keras implementation of U-net, V-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+ with optional ImageNet-trained backbones.

p2p-net icon p2p-net

Official implementation of "Fine-Grained Object Classification via Self-Supervised Pose Alignment".

pytorch-3dunet icon pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

re-net icon re-net

MICCAI 2020 Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network

run-vnet-keras icon run-vnet-keras

Bash and python scripts to run a keras-implemented V-Net 3D segmentation model.

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

segwithdistmap icon segwithdistmap

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

setup-ipsec-vpn icon setup-ipsec-vpn

Scripts to build your own IPsec VPN server, with IPsec/L2TP and Cisco IPsec on Ubuntu, Debian and CentOS

torchio icon torchio

Medical image preprocessing and augmentation toolkit for deep learning

trajectron-plus-plus icon trajectron-plus-plus

Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

unet-pytorch icon unet-pytorch

这是一个unet-pytorch的源码,可以训练自己的模型

vessel3ddl icon vessel3ddl

Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images

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