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Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data.

Patiwet Wuttisarnwattana1,2,3,4, Sansanee Auephanwiriyakul5,6,7

  • 1Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50300, Thailand. patiwet@eng.cmu.ac.th.

Journal of Digital Imaging
|November 28, 2022
PubMed
Summary

Researchers developed a novel algorithm for automatic spleen tissue segmentation in cryo-images, significantly improving efficiency and accuracy for immunological disease research. This new method enhances spleen analysis for graft-versus-host disease and other models.

Keywords:
Biomedical imagingCryo-imagingFuzzy clusteringMedical image analysisSegmentationSpleen

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Area of Science:

  • Immunology
  • Medical Imaging
  • Computational Biology

Background:

  • Spleen tissue segmentation is crucial for studying immunological diseases using cryo-imaging data.
  • Manual segmentation is time-consuming and inefficient, necessitating automated solutions.

Purpose of the Study:

  • To develop a novel, automated algorithm for segmenting spleen substructures (white and red pulp) in cryo-images.
  • To introduce a new Supervised Patch-based Fuzzy c-Mean (spFCM) classifier for enhanced segmentation accuracy.

Main Methods:

  • The algorithm incorporates initial spleen mask creation, feature extraction, spFCM classification, and post-processing.
  • It is specifically designed for datasets generated by advanced cryo-imaging systems enabling single-cell sensitivity.

Main Results:

  • The algorithm achieved accurate and efficient spleen tissue segmentation across various experimental settings.
  • Segmentation throughput was increased by 90-fold compared to manual methods.
  • The spFCM algorithm outperformed traditional classifiers and the U-Net deep-learning model.

Conclusions:

  • This study presents the first explainable algorithm for spleen tissue segmentation in cryo-images.
  • The novel spFCM classifier offers superior performance for spleen analysis.
  • The developed method is valuable for research in graft-versus-host disease and other immunological studies utilizing cryo-imaging.