StarDist_AsPC1_Lifeact

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

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654