A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images

• URL: http://arxiv.org/abs/2306.14875v2
• Date: Thu, 30 Nov 2023 18:28:43 GMT
• Title: A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images
• Authors: Shrijeet Biswas, Amartya Bhattacharya
• Abstract summary: White blood cells, also known as leukocytes are group of heterogeneously nucleated cells which act as salient immune system cells. Leukocytes kill the bacteria, virus and other kind of pathogens which invade human body through phagocytosis. Detection of a white blood cell count can reveal camouflaged infections and warn doctors about chronic medical conditions.
• Score: 0.4532517021515834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: White blood cells, also known as leukocytes are group of heterogeneously nucleated cells which act as salient immune system cells. These are originated in the bone marrow and are found in blood, plasma, and lymph tissues. Leukocytes kill the bacteria, virus and other kind of pathogens which invade human body through phagocytosis that in turn results immunity. Detection of a white blood cell count can reveal camouflaged infections and warn doctors about chronic medical conditions such as autoimmune diseases, immune deficiencies, and blood disorders. Segmentation plays an important role in identification of white blood cells (WBC) from microscopic image analysis. The goal of segmentation in a microscopic image is to divide the image into different distinct regions. In our paper, we tried to propose a novel instance segmentation method for segmenting the WBCs containing both the nucleus and the cytoplasm, from bone marrow images.

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