BrainSec2.0
Optimizing BrainSec
Fork A fork of ucdrubinet/BrainSec2.0; the README may describe the upstream project.
Overview
Figure 1: End-to-End Whole-Slide-Image (WSI) Segmentation Pipeline using SegFormer
BrainSec2.0 is a transformer-based segmentation toolkit for large-scale Whole-Slide Images (WSI) of the human brain. It extends SegFormer (Xie et al., 2021) with LoRA / QLoRA fine-tuning, quantization, and ONNX-based inference for GPU and CPU environments. The toolkit achieves research-grade accuracy while remaining lightweight enough for macOS laptops and low-resource desktops.
Clone the repository and create the Conda environment:
Model weights are large and hosted externally on Google Drive at: https://drive.google.com/drive/folders/1NbLP4E-m5RhgTmHj4mcee1ZR6OcevJIm?usp=sharing
More public WSI data available at:
GPU Inference (≈ 3 min on NVIDIA GPU)
CPU Inference with ONNX (≈ 15 min on M-series)
Finetune the base segformer model using a strategy
From the project’s README on GitHub.
At a glance
| Repository | Kentucky-Open-Science/BrainSec2.0 |
|---|---|
| Research area | Forks of other projects |
| Primary language | None detected |
| License | None specified |
| Stars / forks | 0 / 0 |
| Open issues and pull requests | 0 |
| Created | 2026-08-18 |
| Last push | 2026-08-19 |
| Default branch | main |
| Forked from | ucdrubinet/BrainSec2.0 — Optimizing BrainSec |
What the README covers
- Overview
- Key Features
- Quickstart
- Step 1 — Setup Environment
- Step 2 — Download Models
- Step 3 — Run Inference
- Fine Tuning
- Documentation Report
Top contributors
- @ajinkya-ch (35 commits)
- @ZJUJeffLai (1 commit)
Get the code
git clone https://github.com/Kentucky-Open-Science/BrainSec2.0.git