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PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple

Anubha Gupta1, Pramit Mallick2, Ojaswa Sharma3

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This study introduces the PCSeg Tool for segmenting plasma cells in microscopic images, crucial for diagnosing multiple myeloma (MM). The tool accurately identifies single cells and clusters, advancing automated diagnostic capabilities.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Oncology

Background:

  • Accurate plasma cell segmentation is vital for diagnosing multiple myeloma (MM).
  • Existing segmentation methods lack generalizability across diverse cell types.
  • Automated tools are needed to improve diagnostic efficiency and accuracy.

Purpose of the Study:

  • To present the PCSeg Tool for robust plasma cell segmentation from microscopic medical images.
  • To develop a method applicable to bone marrow aspirate slides from MM patients.
  • To enhance automated diagnostic capabilities for multiple myeloma.

Main Methods:

  • A multi-stage pipeline including pre-processing, a modified multiphase level set method, and post-processing (watershed, circular Hough transform).
  • The modified level set method incorporates prior probability densities of regions of interest in color spaces.
  • Segmentation of regions of interest is achieved using a minimal-partition solution within a two-phase level set formulation.

Main Results:

  • The PCSeg Tool demonstrated effective segmentation of single plasma cells.
  • Efficient segmentation of plasma cell clusters was achieved.
  • The tool provided good segmentation results on various microscopic images.

Conclusions:

  • The PCSeg Tool offers a robust solution for plasma cell segmentation in MM diagnostics.
  • The modified level set approach improves segmentation accuracy by utilizing prior region information.
  • This tool contributes to the development of automated diagnostic systems for multiple myeloma.