This repository includes a StarDist deep learning model designed for segmenting AsPC1 cells labeled with Lifeact from fluorescence microscopy images. The model distinguishes individual AsPC1 cells within clusters and separates them from the background. The model was trained on a small dataset and achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.967, indicating high accuracy in cell segmentation. Specifications Model: StarDist for segmenting AsPC1 cells in fluorescence microscopy images Training Dataset: Number of Images: 10 paired fluorescence microscopy images and label masks Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8 Data Type: Fluorescence microscopy images of the AsPC1 Lifeact channel with manually segmented masks File Format: TIFF (.tif) Fluorescence Images: 16-bit Masks: 8-bit Image Size: 1024 x 1024 pixels (Pixel size: 0.6337 x 0.6337 µm²) Model Capabilities: Segment AsPC1 Cells: Detects individual AsPC1 cells from a cluster and separates them from the background Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity under individual cells in respective channels Performance: Average IoU: 0.884 Average F1 Score: 0.967 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Biorxiv paper

2024