Published August 29, 2024 | Version v1
Dataset Open

StarDist_TumorCell_nuclei

  • 1. ROR icon University of Turku
  • 2. ROR icon Åbo Akademi University

Description

This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei.

Specifications

  • Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel

  • Training Dataset:

    • Number of Images: 48 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 DAPI channel with manually segmented masks

    • File Format: TIFF (.tif)

      • Fluorescence Images: 16-bit

      • Masks: 8-bit

    • Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)

  • Model Capabilities:

    • Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei

    • Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels

  • Performance:

    • Average IoU: 0.558

    • Average F1 Score: 0.793

  • 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

 

Files

Stardist_TCs_nuclei_DAPI.zip

Files (233.5 MB)

Name Size Download all
md5:76a5bcdb1d25b2263e029d5dc966a335
233.5 MB Preview Download