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

Python code to fuse multiple RGB-D images into a TSDF voxel volume.

Fuse multiple depth frames into a TSDF voxel volume.

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

3D Deep Learning works

Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)

Read and write Neuroglancer datasets programmatically.

The implementation of 3D-UNet using PyTorch

Analysis of 3D pathology samples using weakly supervised AI - Cell

ForkNet: Adversarial Semantic Scene Completion from a Single Depth Image - ICCV 2019

Marching Cubes & Mesh Simplification on multi-label 3D images.

[MICCAI'2020 PRIME] Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Severity Estimation.

Compose chunk operators to create a pipeline for local or distributed petabyte-scale computation

Scalable Neuroglancer compatible Downsampling, Meshing, Skeletonizing, Contrast Normalization, Transfers and more.

🤗 AeroPath: An airway segmentation benchmark dataset with challenging pathology

Quanfima (Quantitative Analysis of Fibrous Materials)

[MICCAI 2023] This is the official code for the paper "A Feature-Driven Richardson-Lucy Deconvolution Network"

Matlab codes to create synthetic fractures for multiphase simulation

Volume Data Rendering using GPU Raycasting/Raymarching

Implementation of the Marching Cubes algorithm on Python.