marrlab/SHAPR_torch
SHAPR: Code for "Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction"
SHAPR helps biomedical researchers and cell biologists reconstruct detailed 3D shapes of individual cells or nuclei from standard 2D microscopy images. You input 2D images and their corresponding 2D segmentations, and SHAPR outputs predicted 3D segmentations of the cellular structures. This is ideal for scientists studying cell morphology who need high-throughput 3D information without extensive 3D imaging.
No commits in the last 6 months. Available on PyPI.
Use this if you need to understand the 3D morphology of cells or nuclei but are limited to 2D microscopy images due to throughput or resolution constraints.
Not ideal if you already have high-resolution 3D imaging data and are not focused on increasing throughput from 2D inputs.
Stars
41
Forks
7
Language
Python
License
BSD-3-Clause
Category
Last pushed
Oct 28, 2023
Commits (30d)
0
Dependencies
4
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/marrlab/SHAPR_torch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
openspyrit/spyrit
A Python toolbox for deep image reconstruction, with emphasis on single-pixel imaging.
RobotLocomotion/pytorch-dense-correspondence
Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation"
Fyusion/LLFF
Code release for Local Light Field Fusion at SIGGRAPH 2019
pmh47/dirt
DIRT: a fast differentiable renderer for TensorFlow
natowi/3D-Reconstruction-with-Deep-Learning-Methods
List of projects for 3d reconstruction