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AllenInstitute/
aind-brain-tissue-segmentation

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aind-brain-tissue-segmentation

AIND ｜ 🖥️ GitHub | 🤗 Hugging Face | 🤖 Allen Institute for Neural Dynamics

Brain Tissue Segmentation - Lightsheet Microscopy

In this repository, we present a tool for fast brain tissue segmentation in lightsheet microscopy. This model was trained in entire 2D slices of data coming from our SmartSPIM Lightsheet Microscopes. We used a downsampled resolution corresponding to 14.4 microns in XY and 16.0 microns in Z that then were resized to 1024x1024.

We also provide a script that perform fast inference in entire brains stored in OMEZarr format.

Brain Segmentation Examples

Video Sample 771602

Video Sample 782499

📑 Todo List

Multi-channel segmentation.
Generalization to other microscopes. E.g., (ExASPIM, TissueCyte)

Inference

It supports two processing modes:

Lazy segmentation: For large datasets that cannot fit into memory, images are resized to 1024x1024, normalized, segmented, upsampled, and stored in a Zarr format.
In-memory segmentation: For smaller datasets, a multiscale version is loaded into memory for processing.

The model was trained with a resolution of 14.4 microns in XY and 16 microns in Z.

Whole-brain segmentation script

python large_scale_mask_prediction.py --image_path path/to/your/image.zarr --model_path path/to/your/model.safetensors

Input Parameters

Parameter	Description
`image_path`	Path to the OME-Zarr dataset (local or S3). Example: `s3://bucket/SmartSPIM_Dataset/Ex_639_Em_667.zarr`
`model_path`	Path to the trained segmentation model. It can point to a .cpkt or .safetensors file.
`output_folder`	Directory where results will be saved.
`target_size_mb`	Memory allocation target for large-scale predictions.
`n_workers`	Number of workers for PyTorch DataLoader.
`super_chunksize`	Data shard size for optimized cloud communication.
`scale`	Name of the multiscale dataset to use for segmentation.
`scratch_folder`	Path for temporary files (Raw data).
`image_height`	Resize height for segmentation (Default: `1024`).
`image_width`	Resize width for segmentation (Default: `1024`).
`prob_threshold`	Probability threshold for segmentation mask generation.

Outputs

Output File	Description
`probabilities.zarr`	Zarr dataset containing segmentation probabilities (useful for post-processing).
`segmentation_mask.zarr`	Segmentation mask in the original image space (before resizing).
`data.zarr`	Raw input data used for segmentation (optional).

Installation

Clone the repo:

git clone https://github.com/AllenNeuralDynamics/aind-brain-segmentation
cd aind-brain-segmentation

Install dependencies:

docker build -t brain_seg -f Dockerfile .

AllenInstitute/aind-brain-tissue-segmentation