Keras
microscopy
3d-segmentation
biomedical-image-segmentation
neuron-segmentation
nnunet
stardist
cellpose
sam
Instructions to use Podtyazhki1337/3d-neurons-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Podtyazhki1337/3d-neurons-segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Podtyazhki1337/3d-neurons-segmentation") - Notebooks
- Google Colab
- Kaggle
3D Neuron Segmentation Weights for Oblique and Dodt Gradient-Contrast Microscopy
The code is available here: https://github.com/podtyazhki1337/3d-neurons-segmentation-benchmark This repository contains trained model weights used in the study:
The models were trained for 3D neuron segmentation in label-free microscopy across:
- species: rat, mouse, human
- modalities: oblique illumination and Dodt gradient contrast (DGC / DODT)
Repository contents
The repository includes trained weights for five model families:
- nnU-Net v2
- 3D U-Net
- StarDist3D
- Cellpose
- micro-SAM
Weights are organized into separate folders by model family and training configuration.
- Downloads last month
- 363
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support