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Noel-Niko/
dinov2-upernet-20260322-histology-annotation-human-bigbrain

Name: dinov2-upernet-20260322-histology-annotation-human-bigbrain
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Brain Region Segmentation — Human Brain (BigBrain Tissue Classification)

DINOv2-Large + UperNet model fine-tuned for semantic segmentation of human brain tissue types in histological sections.

Model Details

Attribute	Value
Architecture	DINOv2-Large (304M) + UperNet (38M)
Classes	10 (tissue types)
Input Size	518x518
Training Data	BigBrain 3D histological volume (200um, 9-class tissue classification)
mIoU (center-crop)	60.8%
mIoU (sliding window)	61.3%

Tissue Classes

ID	Class
0	Background
1	Gray Matter
2	White Matter
3	Cerebrospinal Fluid
4	Meninges
5	Blood Vessels
6	Bone/Skull
7	Muscle
8	Artifact
9	Other/Unknown

Usage

bash

git clone https://github.com/Noel-Niko/histological-image-analysis
cd histological-image-analysis
make install
make download-models-human-bigbrain
make annotate-human-bigbrain IMAGES=/path/to/your/slides/

Paper

Cross-Species Transfer of Ultra-Fine-Grained Brain Segmentation: From Mouse to Human with DINOv2 + UperNet

This model is Track B of a three-track human brain segmentation study. It uses the BigBrain 200um classified volume with dense 9-class tissue annotations (Merker stain). The BigBrain model serves as a tissue type classifier — complementary to the Allen depth-3 model's role as a brain region identifier.

See paper.md in this repo for the full paper.

Citation

If you use this model, please cite the training data sources and the paper included in this repository.

Repository

Full source code, training notebooks, and all models: https://github.com/Noel-Niko/histological-image-analysis

Maintaining This Repo

To update model weights, papers, or this README: