Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks

Lian Duan, Xi Qin, Yuanhao He, Xialin Sang, Jinda Pan, Tao Xu, Jing Men, Rudolph E. Tanzi, Airong Li, Yutao Ma, Chao Zhou

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained CNN model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.

Original languageEnglish
Article numbere201800146
JournalJournal of Biophotonics
Volume11
Issue number12
DOIs
StatePublished - Dec 2018

Keywords

  • Drosophila heart
  • deep learning
  • neural networks
  • optical coherence microscopy

Fingerprint

Dive into the research topics of 'Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks'. Together they form a unique fingerprint.

Cite this