Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks
Lian Duan
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorXi Qin
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorYuanhao He
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorXialin Sang
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Department of Electrical Engineering and Computer Science, Hainan University, Haikou, China
Search for more papers by this authorJinda Pan
School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
Search for more papers by this authorTao Xu
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorJing Men
Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorRudolph E. Tanzi
Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
Search for more papers by this authorAirong Li
Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
Search for more papers by this authorYutao Ma
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorCorresponding Author
Chao Zhou
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
Correspondence
Chao Zhou, Department of Electrical and Computer Engineering, Lehigh University, 19 Memorial Drive West, 18015, Bethlehem, Pennsylvania.
Email: [email protected]
Search for more papers by this authorLian Duan
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorXi Qin
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorYuanhao He
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorXialin Sang
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Department of Electrical Engineering and Computer Science, Hainan University, Haikou, China
Search for more papers by this authorJinda Pan
School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
Search for more papers by this authorTao Xu
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorJing Men
Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
Search for more papers by this authorRudolph E. Tanzi
Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
Search for more papers by this authorAirong Li
Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
Search for more papers by this authorYutao Ma
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorCorresponding Author
Chao Zhou
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania
Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania
Correspondence
Chao Zhou, Department of Electrical and Computer Engineering, Lehigh University, 19 Memorial Drive West, 18015, Bethlehem, Pennsylvania.
Email: [email protected]
Search for more papers by this authorAbstract
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.
Supporting Information
| Filename | Description |
|---|---|
| jbio201800146-sup-0001-VideoS1.mp4MPEG-4 video, 3 MB | Video S1 Video of the segmentation result for OCM larva heart images |
| jbio201800146-sup-0002-VideoS2.mp4MPEG-4 video, 3.6 MB | Video S2 Video of the segmentation result for OCM pupa heart images |
| jbio201800146-sup-0003-VideoS3.mp4MPEG-4 video, 3.3 MB | Video S3 Video of the segmentation result for OCM adult heart images |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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