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Multiple myeloma cancer is a type of blood cancer that happens when thegrowth of abnormal plasma cells becomes out of control in the bone marrow.There are various ways to diagnose multiple myeloma in bone marrow such ascomplete blood count test (CBC) or counting myeloma plasma cell in aspirateslide images using manual visualization or through image processing technique.In this work, an automatic deep learning method for the detection andsegmentation of multiple myeloma plasma cell have been explored. To this end, atwo-stage deep learning method is designed. In the first stage, the nucleusdetection network is utilized to extract each instance of a cell of interest.The extracted instance is then fed to the multi-scale function to generate amulti-scale representation. The objective of the multi-scale function is tocapture the shape variation and reduce the effect of object scale on thecytoplasm segmentation network. The generated scales are then fed into apyramid of cytoplasm networks to learn the segmentation map in various scales.On top of the cytoplasm segmentation network, we included a scale aggregationfunction to refine and generate a final prediction. The proposed approach hasbeen evaluated on the SegPC2021 grand-challenge and ranked second on the finaltest phase among all teams.

Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images