MegaQuant: Fully Integrated Deep-Learning Workflow in QuPath—Application to the Detection of Megakaryocytes in Human Bone Marrow
R. Sarkis, L.-F. Celma, R. Dornier, O. Burri, C. Royer-Chardon, M. Barthélemy, A. Alonso, L. de Leval, D. Sage, O. Naveiras
Proceedings of the Twentieth European Congress on Digital Pathology (ECDP'24), Vilnius, Republic of Lithuania, June 5-8, 2024, pp. 29.
Megakaryocytes (MKs), precursors of platelets, can be altered by clinical conditions and their assessment is of diagnostic value. When evaluated on H&E images, they are challenging to segment due to their complex shape. Here, we propose a fully integrated workflow implemented within QuPath, leveraging existing deep-learning tools to segment and quantify MKs.
@INPROCEEDINGS(http://bigwww.epfl.ch/publications/sarkis2401.html,
AUTHOR="Sarkis, R. and Celma, L.-F. and Dornier, R. and Burri, O. and
Royer-Chardon, C. and Barth{\'{e}}lemy, M. and Alonso, A. and de
Leval, L. and Sage, D. and Naveiras, O.",
TITLE="MegaQuant: {F}ully Integrated Deep-Learning Workflow in
QuPath---Application to the Detection of Megakaryocytes in Human
Bone Marrow",
BOOKTITLE="Proceedings of the Twentieth European Congress on Digital
Pathology ({ECDP'24})",
YEAR="2024",
editor="",
volume="",
series="",
pages="29",
address="Vilnius, Republic of Lithuania",
month="June 5-8,",
organization="",
publisher="",
note="")
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