TY - JOUR
T1 - Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images
AU - Ouyang, Xiangping
AU - Matt, Abigail
AU - Wang, Fei
AU - Gracheva, Elena
AU - Migunova, Ekaterina
AU - Rajamani, Saathvika
AU - Dubrovsky, Edward B.
AU - Zhou, Chao
N1 - Publisher Copyright:
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.
AB - Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.
UR - http://www.scopus.com/inward/record.url?scp=85195022321&partnerID=8YFLogxK
U2 - 10.1364/BOE.523364
DO - 10.1364/BOE.523364
M3 - Article
C2 - 38867790
AN - SCOPUS:85195022321
SN - 2156-7085
VL - 15
SP - 3639
EP - 3653
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 6
ER -