A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images
This work addresses a domain-specific problem for medical imaging, potentially aiding in disease diagnosis, but it appears incremental as it builds on existing segmentation techniques without claiming broad breakthroughs.
The paper tackles the problem of segmenting white blood cells from bone marrow images by proposing a novel instance segmentation method that identifies both nucleus and cytoplasm, but no concrete results or numbers are provided in the 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.