🧠 AI-Driven Liver Segmentation for Biomedical Imaging

Supervisor: Prof. Gemma Piella Fenoy
Institute: Universitat Pompeu Fabra (UPF), Barcelona, Spain
Duration: Oct 2025 – Present

This repository contains the complete workflow for developing a mobile-deployable liver boundary segmentation system for point-of-care imaging using RGB smartphone cameras.

The pipeline includes:

• SAM fine-tuning for precise liver segmentation
• Lightweight liver localization using TFLite
• Domain adaptation using color-calibrated data
• Ongoing YOLO-based detection with anatomical priors for robust localization

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🧬 Project Motivation

Early liver disease diagnosis often relies on resource-intensive imaging (CT, MRI).
This work enables low-cost, accessible liver boundary detection using ** consumer-grade cameras**, improving:

• Remote screening
• Telehealth workflows
• Biomedical imaging accessibility in low-resource regions

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📊 Results Summary

Component	Method	Dataset	Performance	Status
Segmentation	Fine-tuned SAM-Vit-Base	Unseen validation	0.90 Dice	Completed
Segmentation — Domain Adapted	SAM + Color-calibrated dataset	Adapted validation	0.91 Dice	Completed
Liver Localization	Lightweight TFLite CNN	Smartphone RGB	0.70 Dice (bbox)	Completed
Detection + Spatial Priors	YOLO-based anatomical guidance	WIP	—	In Progress

⚡ Domain adaptation significantly improved edge precision on dark-skin and low-illumination samples.

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📁 Repository Structure

notebooks/
  finetuning.ipynb   SAM fine-tuning for liver boundary segmentation
  inference.ipynb    Running segmentation/localization inference

🚀 Usage

Open the notebooks in Jupyter or Google Colab:

• notebooks/finetuning.ipynb — fine-tune SAM (ViT-Base) on the liver segmentation dataset.
• notebooks/inference.ipynb — run inference with the fine-tuned model on new images.

A GPU runtime is recommended for both fine-tuning and inference.