  Multiple myeloma cancer is a type of blood cancer that happens when the
growth of abnormal plasma cells becomes out of control in the bone marrow.
There are various ways to diagnose multiple myeloma in bone marrow such as
complete blood count test (CBC) or counting myeloma plasma cell in aspirate
slide images using manual visualization or through image processing technique.
In this work, an automatic deep learning method for the detection and
segmentation of multiple myeloma plasma cell have been explored. To this end, a
two-stage deep learning method is designed. In the first stage, the nucleus
detection 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 a
multi-scale representation. The objective of the multi-scale function is to
capture the shape variation and reduce the effect of object scale on the
cytoplasm segmentation network. The generated scales are then fed into a
pyramid of cytoplasm networks to learn the segmentation map in various scales.
On top of the cytoplasm segmentation network, we included a scale aggregation
function to refine and generate a final prediction. The proposed approach has
been evaluated on the SegPC2021 grand-challenge and ranked second on the final
test phase among all teams.
