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

Bozorgpour Afshin, Azad Reza, Showkatian Eman, Sulaiman Alaa
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:47-56, 2021.

Abstract

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 a complete blood count test (CBC) or counting myeloma plasma cells in aspirate slide images using manual visualization or image processing techniques. In this work, an automatic deep learning method for detecting and segmentation multiple myeloma plasma cells has 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-afshin21a, title = {Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images}, author = {Afshin, Bozorgpour and Reza, Azad and Eman, Showkatian and Alaa, Sulaiman}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {47--56}, year = {2021}, editor = {Atzori, Manfredo and Burlutskiy, Nikolay and Ciompi, Francesco and Li, Zhang and Minhas, Fayyaz and Müller, Henning and Peng, Tingying and Rajpoot, Nasir and Torben-Nielsen, Ben and van der Laak, Jeroen and Veta, Mitko and Yuan, Yinyin and Zlobec, Inti}, volume = {156}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v156/afshin21a/afshin21a.pdf}, url = {https://proceedings.mlr.press/v156/afshin21a.html}, abstract = {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 a complete blood count test (CBC) or counting myeloma plasma cells in aspirate slide images using manual visualization or image processing techniques. In this work, an automatic deep learning method for detecting and segmentation multiple myeloma plasma cells has 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.} }
Endnote
%0 Conference Paper %T Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images %A Bozorgpour Afshin %A Azad Reza %A Showkatian Eman %A Sulaiman Alaa %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2021 %E Manfredo Atzori %E Nikolay Burlutskiy %E Francesco Ciompi %E Zhang Li %E Fayyaz Minhas %E Henning Müller %E Tingying Peng %E Nasir Rajpoot %E Ben Torben-Nielsen %E Jeroen van der Laak %E Mitko Veta %E Yinyin Yuan %E Inti Zlobec %F pmlr-v156-afshin21a %I PMLR %P 47--56 %U https://proceedings.mlr.press/v156/afshin21a.html %V 156 %X 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 a complete blood count test (CBC) or counting myeloma plasma cells in aspirate slide images using manual visualization or image processing techniques. In this work, an automatic deep learning method for detecting and segmentation multiple myeloma plasma cells has 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.
APA
Afshin, B., Reza, A., Eman, S. & Alaa, S.. (2021). Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:47-56 Available from https://proceedings.mlr.press/v156/afshin21a.html.

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