BrainSec2.0

Source code on GitHub.

README

🧠 BrainSec2.0 — WSI Segmentation Toolkit

Figure 1: End-to-End Whole-Slide-Image (WSI) Segmentation Pipeline using SegFormer

Overview

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.

Key Features

| Category | Description | |-----------|--------------| | Segmentation | White- and gray-matter segmentation on high-resolution WSIs | | Fine-Tuning | Four LoRA / QLoRA configurations for flexible adaptation | | Performance | Full-slide inference in ≈ 3 min (GPU) or ≈ 15 min (CPU / M-series Mac) | | Deployment | Quantized ONNX export for efficient CPU inference | | Platform Support | macOS, Linux, NVIDIA GPUs, and HPC clusters | | Ease of Use | Plug-and-play scripts + reproducible Conda environment |

Quickstart

Step 1 — Setup Environment

Clone the repository and create the Conda environment: ``bash conda env create -f configs/server_env.yml -n brainsec2 conda activate brainsec2 `

Step 2 — Download Models

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:

https://zenodo.org/records/1470797 (.svs)

https://datadryad.org/dataset/doi:10.5061/dryad.7h44j107j (.czi)

`bash mkdir -p models unzip ~/Downloads/model.zip -d models/ `

Step 3 — Run Inference

GPU Inference (≈ 3 min on NVIDIA GPU) `bash python src/inference/inference_main.py --model-type pretrained --model-dir models/segformer_pretrained --wsi-path data/sample.svs --output output.png --batch-size 8 `

CPU Inference with ONNX (≈ 15 min on M-series) `bash python src/onnxformatting/inference_onnx.py --onnx-model models/segformer_lora.onnx --wsi-path data/sample.svs --output output_onnx.png --executor-provider CPUExecutionProvider `

Fine Tuning

Finetune the base segformer model using a strategy `bash python src/inference/inference_main.py --model-type lora --model-dir models/ft_models/finetuned_lora --base-model-dir models/segformer_pretrained --wsi-path data/sample.svs ``

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