{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:28:53Z","timestamp":1762432133283,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Multiple 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.<\/jats:p>","DOI":"10.3390\/e24010134","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T08:20:42Z","timestamp":1642407642000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5071-1535","authenticated-orcid":false,"given":"May Phu","family":"Paing","sequence":"first","affiliation":[{"name":"School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adna","family":"Sento","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Thai-Nichi Institute of Technology, Bangkok 10250, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2449-5653","authenticated-orcid":false,"given":"Toan Huy","family":"Bui","sequence":"additional","affiliation":[{"name":"Course of Science and Technology, Graduate School of Science and Technology, Tokai University, Tokyo 108-8619, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuchart","family":"Pintavirooj","sequence":"additional","affiliation":[{"name":"School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","unstructured":"Bozorgpour, A., Azad, R., and Showkatian, E. (2021). Multi-Scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Padala, S.A., Barsouk, A., Barsouk, A., Rawla, P., Vakiti, A., Kolhe, R., Kota, V., and Ajebo, G.H. (2021). Epidemiology, Staging, and Management of Multiple Myeloma. Med. Sci., 9.","DOI":"10.3390\/medsci9010003"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vyshnav, M.T., Sowmya, V., Gopalakrishnan, E.A., Variyar, V.V.S., Menon, V.K., and Soman, K.P. (2020, January 1\u20133). Deep Learning Based Approach for Multiple Myeloma Detection. Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT49239.2020.9225651"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gupta, A., Mallick, P., Sharma, O., Gupta, R., and Duggal, R. (2018). 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