README.md

---
license: mit
tags:
  - pytorch
  - unet
  - image-segmentation
  - biomedical
  - fluorescence-microscopy
  - amyloid
  - fibril
  - research
library_name: segmentation_models.pytorch
inference: false
datasets: []
language: []
model-index:
  - name: UNet Fibril Segmentation
    results: []
---


# 🧠 UNet Fibril Segmentation Model

![UNet Fibril Segmentation Banner](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/model-card-image-placeholder.png)

A **UNet-based deep learning model** trained for **semantic segmentation of fibrillar structures** in **single-molecule fluorescence microscopy images**. This model is specifically designed to identify and segment **amyloid fibrils**, which are critical in research related to neurodegenerative diseases such as Alzheimer’s.

---

## 🔬 Model Overview

- **Architecture**: [UNet](https://arxiv.org/abs/1505.04597)
- **Encoder**: ResNet34 (pretrained on ImageNet)
- **Input Channels**: 1 (grayscale)
- **Output**: Binary mask of fibril regions
- **Loss Function**: BCEWithLogitsLoss / Dice Loss (combined)
- **Framework**: PyTorch + [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)

---

## 🧠 Use Case

The model is built for biomedical researchers and computer vision practitioners working in:

- **Neuroscience research** (e.g., Alzheimer's, Parkinson’s)
- **Amyloid aggregation studies**
- **Single-molecule fluorescence microscopy**
- **Self-supervised denoising + segmentation pipelines**

---

## 🧪 Dataset

The model was trained on a curated dataset of **fluorescence microscopy images** annotated for fibrillar structures. Images were grayscale, of size `512x512` or `256x256`, manually labeled using Fiji/ImageJ or custom annotation tools.

*Note: If you're a researcher and would like to contribute more annotated data or collaborate on a dataset release, please reach out.*

---

## 📦 Files

- `unet_fibril_seg_model.pth` — Trained PyTorch weights
- `inference.py` — Inference script for running the model
- `preprocessing.py` — Image normalization and transforms

---

## 🖼️ Example

```python
import torch
import numpy as np
from PIL import Image
from torchvision import transforms
import segmentation_models_pytorch as smp

# Load model
model = smp.Unet(
    encoder_name="resnet34",
    encoder_weights="imagenet",
    in_channels=1,
    classes=1,
)
model.load_state_dict(torch.load("unet_fibril_seg_model.pth", map_location="cpu"))
model.eval()

# Load image
img = Image.open("test_image.png").convert("L")
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
input_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    pred = model(input_tensor)
    pred_mask = torch.sigmoid(pred).squeeze().numpy()
    binary_mask = (pred_mask > 0.5).astype(np.uint8)

# Save output
Image.fromarray(binary_mask * 255).save("predicted_mask.png")