Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)
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Updated
Jun 10, 2024 - C++
Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)
Scalable Neuroglancer compatible Downsampling, Meshing, Skeletonizing, Contrast Normalization, Transfers and more.
Read and write Neuroglancer datasets programmatically.
3D U-Net model for volumetric semantic segmentation written in pytorch
Image pyramid generation for grayscale and segmentation image resize.
Analysis of 3D pathology samples using weakly supervised AI - Cell
Compose chunk operators to create a pipeline for local or distributed petabyte-scale computation
Quanfima (Quantitative Analysis of Fibrous Materials)
A volume processing toolbox written in C++ and CUDA. Works as standalone C++ application or with a dedicated GUI.
Relatively flexible examples for building DNNs with volumetric data in pytorch
🫁 AeroPath: An airway segmentation benchmark dataset with challenging pathology
Marching Cubes & Mesh Simplification on multi-label 3D images.
A Feature-Driven Richardson-Lucy Deconvolution Network
3D reaction diffusion project using WebGL 2.0 and ray casting to visualize the volume data.
First Person Bioimage
A set of tools for BVP (Blocky Volume Package)
Make brain montages with and without outlines
Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data
Python code to fuse multiple RGB-D images into a TSDF voxel volume.
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