# black0017/MedicalZooPytorch

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

Repository: https://github.com/black0017/MedicalZooPytorch
Canonical: https://ross.abutalabs.com/products/medicalzoopytorch
Language: Python
License: MIT
License Family: permissive
Topics: segmentation, deep-learning, pytorch, medical-imaging, medical-image-processing, medical-image-segmentation, 3d-convolutional-network, brats2019, brats2018, densenet, resnet, iseg-challenge, iseg, mrbrains18, unet, unet-image-segmentation, segmentation-models
Last push: 2024-07-25T10:58:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2598, "days_push": 769, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1917, forks 305 (observed 2026-08-28T04:05:53.912986+00:00)

## What it is
A PyTorch-based open-source library implementing state-of-the-art 3D (and some 2D) deep neural networks for multi-modal medical image segmentation, with data loaders for common medical imaging datasets like BraTS and MRBrains. It aims to support reproducible deep learning research in medical imaging, originally focused on 3D multi-modal brain MRI segmentation.

## Use cases
- segment 3D brain MRI scans with deep learning
- train a U-Net on the BraTS tumor segmentation dataset
- run multi-modal medical image segmentation in PyTorch
- get data loaders for medical imaging challenge datasets
- compare 3D segmentation architectures like V-Net and UNet
- apply deep learning to medical image volumes

## When to choose
- you need ready-made 3D segmentation architectures in PyTorch for medical images
- you are working with brain MRI datasets like BraTS, ISEG, or MRBrains
- you want reproducible baselines for medical image segmentation research

## When to avoid
- you need general-purpose (non-medical) image segmentation
- you require production-grade clinical deployment tooling
- you need 2D-only lightweight segmentation models with broad dataset support

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, machine-learning, healthcare, image-processing
- platform: python
- tags: medical-imaging, image-segmentation, pytorch, 3d-segmentation, mri, brain-segmentation, unet, medical-image-dataloaders, gpu

## Member repositories
- black0017/MedicalZooPytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.912986+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:10:08.651612+00:00, confidence not recorded.
  - readme: https://github.com/black0017/MedicalZooPytorch (fetched 2026-08-28T04:05:53.912986+00:00, sha 481bfcae1dd9)
- Data as of 2026-08-30T08:39:29.467469+00:00.