Read and write Neuroglancer datasets programmatically.
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Updated
Jun 5, 2024 - Python
Read and write Neuroglancer datasets programmatically.
3D U-Net model for volumetric semantic segmentation written in pytorch
Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)
Scalable Neuroglancer compatible Downsampling, Meshing, Skeletonizing, Contrast Normalization, Transfers and more.
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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