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

Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma...

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Bibliographic Details
Published in:PloS one 2018-12, Vol.13 (12), p.e0207908-e0207908
Main Authors: Gupta, Anubha, Mallick, Pramit, Sharma, Ojaswa, Gupta, Ritu, Duggal, Rahul
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Biology and Life Sciences
Bone marrow
Clusters
Color
Cytoplasm
Diagnosis, Computer-Assisted - methods
Diagnostic software
Diagnostic systems
Hough transformation
Humans
Image processing
Image Processing, Computer-Assisted
Image segmentation
Information processing
Information technology
Medical imaging
Medicine and Health Sciences
Methods
Microscopy
Morphology
Multiphase
Multiple myeloma
Multiple Myeloma - diagnosis
Multiple Myeloma - pathology
Pattern Recognition, Automated - methods
Physical Sciences
Plasma
Plasma Cells - classification
Plasma Cells - cytology
Post-production processing
Research and Analysis Methods
Software
Statistical analysis
Watersheds
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Summary:Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0207908