Machine learning is commonly used in biomedical image analysis, as it allows automated image segmentation and identification that minimizes the need for tedious human involvement. Drosophila melanogaster is often used as a cardiac disease model, where optical coherence microscopy (OCM) is used to image and analyze its beating dynamics. As OCM often generates a large volume of images, automated image segmentation is necessary to quantify the heart beating efficiently. Our most recent heart segmentation model, FlyNet 2.0+, is a fully convolutional LSTM U-Net model. However, the performance of the model diminishes in the presence of artifacts, such as image reflection and heart movement, resulting in time-consuming manual intervention for mask correction. Therefore, we developed the FlyNet 3.0 model with integrated attention gates in skip connections between each level of the LSTM U-Net model. The attention model adaptively adjusts and automatically learns to focus on the target structure, the heart area. Compared to the previous model, Flynet 3.0 increases the prediction intersection over union (IOU) accuracy from 0.86 to 0.89 for images with reflection artifacts and from 0.81 to 0.89 for those depicting heart movement. Furthermore, we have expanded the functionalities of OCM analyses through automated and dynamic heart wall thickness measurements, which we have validated using a Drosophila model of cardiac hypertrophy.
","abstract_html":"<p>Machine learning is commonly used in biomedical image analysis, as it allows automated image segmentation and identification that minimizes the need for tedious human involvement. <em>Drosophila melanogaster</em> is often used as a cardiac disease model, where optical coherence microscopy (OCM) is used to image and analyze its beating dynamics. As OCM often generates a large volume of images, automated image segmentation is necessary to quantify the heart beating efficiently. Our most recent heart segmentation model, FlyNet 2.0+, is a fully convolutional LSTM U-Net model. However, the performance of the model diminishes in the presence of artifacts, such as image reflection and heart movement, resulting in time-consuming manual intervention for mask correction. Therefore, we developed the FlyNet 3.0 model with integrated attention gates in skip connections between each level of the LSTM U-Net model. The attention model adaptively adjusts and automatically learns to focus on the target structure, the heart area. Compared to the previous model, Flynet 3.0 increases the prediction intersection over union (IOU) accuracy from 0.86 to 0.89 for images with reflection artifacts and from 0.81 to 0.89 for those depicting heart movement. Furthermore, we have expanded the functionalities of OCM analyses through automated and dynamic heart wall thickness measurements, which we have validated using a <em>Drosophila</em> model of cardiac hypertrophy.</p>","abstract_has_math":false,"creators":["Ouyang, Xiangping"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Chao Zhou","Song Hu Quing Zhu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-11T07:00:00Z","date_published":"2024-05-11T07:00:00Z","updated_at":"2026-07-24T06:13:55Z","subjects":["Drosophila","Convolutional Neural Network","Optical Coherence Tomography","LSTM","attention model","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Computational Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/1024"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/1024","href":"https://openscholarship.wustl.edu/eng_etds/1024","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/m8s5-zs85","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chao Zhou","Song Hu Quing Zhu"]},{"key":"dc:creator","label":"Author","values":["Ouyang, Xiangping"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-11-06T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Drosophila","Convolutional Neural Network","Optical Coherence Tomography","LSTM","attention model","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Computational Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/m8s5-zs85","https://openscholarship.wustl.edu/eng_etds/1024"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Machine learning is commonly used in biomedical image analysis, as it allows automated image segmentation and identification that minimizes the need for tedious human involvement. Drosophila melanogaster is often used as a cardiac disease model, where optical coherence microscopy (OCM) is used to image and analyze its beating dynamics. As OCM often generates a large volume of images, automated image segmentation is necessary to quantify the heart beating efficiently. Our most recent heart segmentation model, FlyNet 2.0+, is a fully convolutional LSTM U-Net model. However, the performance of the model diminishes in the presence of artifacts, such as image reflection and heart movement, resulting in time-consuming manual intervention for mask correction. Therefore, we developed the FlyNet 3.0 model with integrated attention gates in skip connections between each level of the LSTM U-Net model. The attention model adaptively adjusts and automatically learns to focus on the target structure, the heart area. Compared to the previous model, Flynet 3.0 increases the prediction intersection over union (IOU) accuracy from 0.86 to 0.89 for images with reflection artifacts and from 0.81 to 0.89 for those depicting heart movement. Furthermore, we have expanded the functionalities of OCM analyses through automated and dynamic heart wall thickness measurements, which we have validated using a Drosophila model of cardiac hypertrophy.
"]},{"key":"dc:title","label":"Title","values":["An Attention LSTM U-Net Model for Drosophila Melanogaster Heart Tube Segmentation"]}]}],"canonical_facts":{"dc:contributor":["Chao Zhou","Song Hu Quing Zhu"],"dc:creator":["Ouyang, Xiangping"],"dc:date.available":["2024-11-06T08:00:00Z"],"dc:description.abstract":["Machine learning is commonly used in biomedical image analysis, as it allows automated image segmentation and identification that minimizes the need for tedious human involvement. Drosophila melanogaster is often used as a cardiac disease model, where optical coherence microscopy (OCM) is used to image and analyze its beating dynamics. As OCM often generates a large volume of images, automated image segmentation is necessary to quantify the heart beating efficiently. Our most recent heart segmentation model, FlyNet 2.0+, is a fully convolutional LSTM U-Net model. However, the performance of the model diminishes in the presence of artifacts, such as image reflection and heart movement, resulting in time-consuming manual intervention for mask correction. Therefore, we developed the FlyNet 3.0 model with integrated attention gates in skip connections between each level of the LSTM U-Net model. The attention model adaptively adjusts and automatically learns to focus on the target structure, the heart area. Compared to the previous model, Flynet 3.0 increases the prediction intersection over union (IOU) accuracy from 0.86 to 0.89 for images with reflection artifacts and from 0.81 to 0.89 for those depicting heart movement. Furthermore, we have expanded the functionalities of OCM analyses through automated and dynamic heart wall thickness measurements, which we have validated using a Drosophila model of cardiac hypertrophy.
"],"dc:identifier":["https://doi.org/10.7936/m8s5-zs85","https://openscholarship.wustl.edu/eng_etds/1024"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"dc:subject":["Drosophila","Convolutional Neural Network","Optical Coherence Tomography","LSTM","attention model","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Computational Engineering"],"dc:title":["An Attention LSTM U-Net Model for Drosophila Melanogaster Heart Tube Segmentation"],"thesis:degree_discipline":["Computer Science & Engineering","McKelvey School of Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T06:13:55Z"}