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Quantifying structural heterogeneity of healthy and cancerous mitochondria using a combined segmentation and classification USK-net.

Lect. Notes Comput. Sc. 11731 LNCS, 289-298 (2019)
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Mitochondria are the main source of cellular energy and thus essential for cell survival. Pathological conditions like cancer, can cause functional alterations and lead to mitochondrial dysfunction. Indeed, electron micrographs of mitochondria that are isolated from cancer cells show a different morphology as compared to mitochondria from healthy cells. However, the description of mitochondrial morphology and the classification of the respective samples are so far qualitative. Furthermore, large intra-class variability and impurities such as mitochondrial fragments and other organelles in the micrographs make a clear separation between healthy and cancerous samples challenging. In this study, we propose a deep-learning based model to quantitatively assess the status of each intact mitochondrion with a continuous score, which measures its closeness to the healthy/tumor classes based on its morphology. This allows us to describe the structural transition from healthy to cancerous mitochondria. Methodologically, we train two USK networks, one to segment individual mitochondria from an electron micrograph, and the other to softly classify each image pixel as belonging to (i) healthy mitochondrial, (ii) cancerous mitochondrial and (iii) non-mitochondrial (image background & impurities) tissue. Our combined model outperforms each network alone in both pixel classification and object segmentation. Moreover, our model can quantitatively assess the mitochondrial heterogeneity within and between healthy samples and different tumor types, hence providing insightful information of mitochondrial alterations in cancer development.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cancer ; Classification ; Convolutional Neural Network ; Deep Learning ; Mitochondria ; Segmentation ; Usk-net
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Quellenangaben Band: 11731 LNCS, Heft: , Seiten: 289-298 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]