🔬 Cellpose vs CellSAM: Microscopy Segmentation Benchmark

This repository contains the initial setup and code for benchmarking Cellpose and CellSAM on microscopy image datasets.

The aim is to evaluate how well each model performs for cell segmentation tasks using ground truth annotations, focusing on real-world datasets like LIVECell and S-BIAD634.

📁 Project Structure

• images/ – Raw microscopy images
• masks/ – Ground truth segmentation masks
• cellpose_results/ – Outputs from Cellpose
• cellsam_results/ – Outputs from CellSAM
• notebooks/ – Jupyter/Colab notebooks for inference and comparison
• utils/ – Helper functions for visualisation and evaluation

🛠️ Tools & Libraries

• Python, OpenCV, NumPy, Matplotlib
• Cellpose (pip install cellpose)
• Segment Anything / CellSAM
• Google Colab (recommended for GPU-based runs)

🚀 Current Focus

• Currently in intial phase for data exploration and baseline model testing

🧠 Key References

• 🔹 Cellpose: A Generalist Algorithm for Cellular Segmentation
  Stringer et al., 2021 – https://doi.org/10.1038/s41592-020-01018-x

• 🔹 Segment Anything
  Kirillov et al., Meta AI, 2023 – https://arxiv.org/abs/2304.02643

• 🔹 CellSAM: Segment Anything in Microscopy
  S. Ji et al., 2023 – https://arxiv.org/abs/2306.00989

• 🔹 Cellpose 2.0: How to Train Your Own Model
  Pachitariu et al., 2022 – https://doi.org/10.1101/2022.04.01.486764

• 🔹 SAM-Cell: A Microscopy-Tuned Version of SAM (Community Repo)
  https://github.com/saikat2019/SAM-Cell

---

📌 This project is part of my MSc dissertation work at Queen’s University Belfast.