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Deep Semantic Segmentation of Angiogenesis Images.

Alisher Ibragimov1, Sofya Senotrusova1, Kseniia Markova2

  • 1Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), 109004 Moscow, Russia.

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|January 21, 2023
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Summary

This study introduces an automated tool for analyzing in vitro angiogenesis images, significantly reducing manual effort. It presents the first deep learning application for semantic segmentation of capillary-like structures, alongside a new public dataset.

Keywords:
angiogenesisdeep learningendothelial cellssemantic segmentation

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Medical Imaging

Background:

  • Angiogenesis, the formation of new blood vessels, is vital for human health.
  • In vitro endothelial cell culture on Matrigel is a common method to study angiogenesis.
  • Manual analysis of microphotographs in these studies is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop an automated technique for annotating images of capillary-like structures in vitro.
  • To address the limitations of manual image analysis in angiogenesis research.
  • To apply deep learning for semantic segmentation of angiogenesis images.

Main Methods:

  • Development of a tool utilizing a convolutional Unet++ encoder-decoder architecture.
  • Semantic segmentation of in vitro angiogenesis simulation images.
  • Postprocessing of segmentation masks for expert data analysis.
  • Creation and public release of the first annotated dataset in this field, AngioCells.

Main Results:

  • Demonstration of the first deep learning-based tool for semantic segmentation of in vitro angiogenesis images.
  • Introduction of the AngioCells dataset, the first publicly available annotated dataset for angiogenesis image analysis.
  • Establishment of a method to automate the analysis of capillary-like structures.

Conclusions:

  • The developed tool and dataset offer a significant advancement in the automation of angiogenesis research.
  • Deep learning, specifically the Unet++ architecture, is effective for semantic segmentation of angiogenesis images.
  • Automated analysis will accelerate research and improve the efficiency of studying this crucial biological process.