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---
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
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")
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