Structure-based assessment of cancerous mitochondria using deep networks
Mishra M, Schmitt S, Wang L, Strasser MK, Marr C, Navab N, Zischka H, Peng T (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: IEEE Computer Society
Book Volume: 2016-June
Pages Range: 545-548
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Prague, CZE
ISBN: 9781479923502
DOI: 10.1109/ISBI.2016.7493327
Abstract
Mitochondrial functions are essential for cell survival. Pathologic situations, e.g. cancer, can impair mitochondrial function which is frequently reflected by an altered morphology. So far, feature description of mitochondrial structure in cancer remains largely qualitative. In this study, we propose a learning-based approach to quantitatively assess the structure of mitochondria isolated from liver tumor cell lines using convolutional neural network (CNN). Besides achieving a high classification accuracy on isolated mitochondria from healthy tissue and different tumor cell lines which the CNN model was trained on, CNN is also able to classify unseen tumor cell lines, which suggests its superior capability to capture the intrinsic structural transition from healthy to tumor mitochondria.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
How to cite
APA:
Mishra, M., Schmitt, S., Wang, L., Strasser, M.K., Marr, C., Navab, N.,... Peng, T. (2016). Structure-based assessment of cancerous mitochondria using deep networks. In Proceedings - International Symposium on Biomedical Imaging (pp. 545-548). Prague, CZE: IEEE Computer Society.
MLA:
Mishra, Manish, et al. "Structure-based assessment of cancerous mitochondria using deep networks." Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, CZE IEEE Computer Society, 2016. 545-548.
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