Automated drosophila heartbeat counting based on image segmentation technique on optical coherence tomography

Chia Yen Lee*, Hao Jen Wang, Jheng Da Jhang, I. Chun Cho

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

5 Scopus citations

Abstract

Drosophila and human cardiac genes are very similar. Biological parametric studies on drosophila cardiac have improved our understanding of human cardiovascular disease. Drosophila cardiac consist of five circular chambers: a conical chamber (CC) and four ostia sections (O1–O4). Due to noise and grayscale discontinuity on optical coherence tomography (OCT) images, previous researches used manual counting or M-mode to analyze heartbeats, which are inefficient and time-consuming. An automated drosophila heartbeat counting algorithm based on the chamber segmentation is developed for OCT in this study. This algorithm has two parts: automated chamber segmentation and heartbeat counting. In addition, this study proposes a principal components analysis (PCA)-based supervised learning method for training the chamber contours to make chamber segmentation more accurate. The mean distances between the conical, second and third chambers attained by the proposed algorithm and the corresponding manually delineated boundaries defined by two experts were 1.26 ± 0.25, 1.47 ± 1.25 and 0.84 ± 0.60 (pixels), respectively. The area overlap similarities were 0.83 ± 0.09, 0.75 ± 0.11 and 0.74 ± 0.12 (pixels), respectively. The average calculated heart rates of two-week and six-week drosophila were about 4.77 beats/s and 4.73 beats/s, respectively, which was consistent with the results of manual counting.

Original languageEnglish
Article number5557
JournalScientific Reports
Volume9
Issue number1
DOIs
StatePublished - 01 12 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019, The Author(s).

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