Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

dc.contributor.authorPaing, May Phu
dc.contributor.authorSento, Adna
dc.contributor.authorBui, Toan Huy
dc.contributor.authorPintavirooj, Chuchart
dc.date.accessioned2026-08-06T10:36:04Z
dc.date.available2026-08-06T10:36:04Z
dc.date.issued2022-01-01
dc.description.abstractMultiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.
dc.identifier.citationEntropy, 24(1), 2022
dc.identifier.doi10.3390/e24010134
dc.identifier.issn10994300
dc.identifier.other2-s2.0-85123175618
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12972
dc.sourceEntropy
dc.subjectData augmentation
dc.subjectDeep learning
dc.subjectMask R-CNN
dc.subjectMultiple myeloma
dc.subjectPlasma cells
dc.titleInstance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN
dc.typeArticle

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