Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography
Congenital heart disease, the most common birth defect in newborns and children, highlights the significance of understanding heart development. In the early development stage, the biomechanical environment, especially wall shear stress (WSS), plays a crucial role in heart morphogenesis. The outflow...
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| Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-11 |
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| creator | Song, Baihang Jiang, Huiwen Liu, Jian Yu, Yao Luan, Jingmin Zhao, Yuqian Wang, Yi Zhang, Jingyuan Liu, Zhao Zhang, Ning Zhu, Xin Ma, Zhenhe |
| description | Congenital heart disease, the most common birth defect in newborns and children, highlights the significance of understanding heart development. In the early development stage, the biomechanical environment, especially wall shear stress (WSS), plays a crucial role in heart morphogenesis. The outflow tract (OFT) is an important segment of the embryonic heart, and a large portion of congenital heart defects originate in the OFT. However, real-time measurement of WSS in the OFT of animal models remains challenging. We propose an automatic-localization segmentation network (ALSegNet) integrated with spectral domain optical coherence tomography (SD-OCT) to achieve real-time WSS calculation. Our ALSegNet accurately extracts the blood flow area from the SD-OCT structure image by incorporating an automatic localization module. WSS calculation is performed with the combination of extracted flow area and flow velocity information provided by SD-OCT. The network achieves segmentation within 22.2 ms, making the total WSS calculation time approximately 36 ms for each B-scan. Our approach achieves a speed of 27 frames/s, adequate for video display. Using the developed system, we successfully monitor in vivo WSS in the OFT of chicken embryos, providing valuable insights into embryonic heart development. The integration of deep learning with SD-OCT enables real-time and accurate WSS measurements, offering a powerful tool for studying congenital heart disease and heart morphogenesis. |
| doi_str_mv | 10.1109/TIM.2024.3417602 |
| format | Article |
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In the early development stage, the biomechanical environment, especially wall shear stress (WSS), plays a crucial role in heart morphogenesis. The outflow tract (OFT) is an important segment of the embryonic heart, and a large portion of congenital heart defects originate in the OFT. However, real-time measurement of WSS in the OFT of animal models remains challenging. We propose an automatic-localization segmentation network (ALSegNet) integrated with spectral domain optical coherence tomography (SD-OCT) to achieve real-time WSS calculation. Our ALSegNet accurately extracts the blood flow area from the SD-OCT structure image by incorporating an automatic localization module. WSS calculation is performed with the combination of extracted flow area and flow velocity information provided by SD-OCT. The network achieves segmentation within 22.2 ms, making the total WSS calculation time approximately 36 ms for each B-scan. Our approach achieves a speed of 27 frames/s, adequate for video display. Using the developed system, we successfully monitor in vivo WSS in the OFT of chicken embryos, providing valuable insights into embryonic heart development. The integration of deep learning with SD-OCT enables real-time and accurate WSS measurements, offering a powerful tool for studying congenital heart disease and heart morphogenesis.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2024.3417602</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Biomechanics ; Biomedical measurement ; Blood ; Blood flow ; Cardiovascular disease ; Congenital diseases ; Convolutional neural network (CNN) ; Deep learning ; Defects ; Diseases ; Embryo ; Embryos ; Flow velocity ; Heart ; Heart diseases ; Image segmentation ; Localization ; Location awareness ; Morphogenesis ; object detection ; Optical Coherence Tomography ; Real time ; Real-time systems ; semantic segmentation ; Shear stress ; spectral-domain optical coherence tomography (SD-OCT) ; Stress measurement ; Time measurement ; Tomography ; wall shear stress (WSS) ; Wall shear stresses</subject><ispartof>IEEE transactions on instrumentation and measurement, 2024, Vol.73, p.1-11</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c175t-6c070ffffc84006d990e2c8445170ca17586e8cd32fd83cd077438365fbfef483</cites><orcidid>0009-0005-6437-9560 ; 0000-0003-4079-9550 ; 0000-0002-4376-0806 ; 0000-0002-9754-5597 ; 0009-0003-2954-0350 ; 0009-0003-7612-4982 ; 0009-0000-4278-6169 ; 0000-0001-5904-6415 ; 0000-0003-1476-0732 ; 0000-0002-7933-055X ; 0009-0000-8454-9436 ; 0009-0003-7477-8704</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10568221$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10568221$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Song, Baihang</creatorcontrib><creatorcontrib>Jiang, Huiwen</creatorcontrib><creatorcontrib>Liu, Jian</creatorcontrib><creatorcontrib>Yu, Yao</creatorcontrib><creatorcontrib>Luan, Jingmin</creatorcontrib><creatorcontrib>Zhao, Yuqian</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Zhang, Jingyuan</creatorcontrib><creatorcontrib>Liu, Zhao</creatorcontrib><creatorcontrib>Zhang, Ning</creatorcontrib><creatorcontrib>Zhu, Xin</creatorcontrib><creatorcontrib>Ma, Zhenhe</creatorcontrib><title>Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>Congenital heart disease, the most common birth defect in newborns and children, highlights the significance of understanding heart development. In the early development stage, the biomechanical environment, especially wall shear stress (WSS), plays a crucial role in heart morphogenesis. The outflow tract (OFT) is an important segment of the embryonic heart, and a large portion of congenital heart defects originate in the OFT. However, real-time measurement of WSS in the OFT of animal models remains challenging. We propose an automatic-localization segmentation network (ALSegNet) integrated with spectral domain optical coherence tomography (SD-OCT) to achieve real-time WSS calculation. Our ALSegNet accurately extracts the blood flow area from the SD-OCT structure image by incorporating an automatic localization module. WSS calculation is performed with the combination of extracted flow area and flow velocity information provided by SD-OCT. The network achieves segmentation within 22.2 ms, making the total WSS calculation time approximately 36 ms for each B-scan. Our approach achieves a speed of 27 frames/s, adequate for video display. Using the developed system, we successfully monitor in vivo WSS in the OFT of chicken embryos, providing valuable insights into embryonic heart development. The integration of deep learning with SD-OCT enables real-time and accurate WSS measurements, offering a powerful tool for studying congenital heart disease and heart morphogenesis.</description><subject>Biomechanics</subject><subject>Biomedical measurement</subject><subject>Blood</subject><subject>Blood flow</subject><subject>Cardiovascular disease</subject><subject>Congenital diseases</subject><subject>Convolutional neural network (CNN)</subject><subject>Deep learning</subject><subject>Defects</subject><subject>Diseases</subject><subject>Embryo</subject><subject>Embryos</subject><subject>Flow velocity</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>Image segmentation</subject><subject>Localization</subject><subject>Location awareness</subject><subject>Morphogenesis</subject><subject>object detection</subject><subject>Optical Coherence Tomography</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>semantic segmentation</subject><subject>Shear stress</subject><subject>spectral-domain optical coherence tomography (SD-OCT)</subject><subject>Stress measurement</subject><subject>Time measurement</subject><subject>Tomography</subject><subject>wall shear stress (WSS)</subject><subject>Wall shear stresses</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkD1PwzAQhi0EEqWwMzBYYk45O4ntjFX5aKVWlWgrxsg4F5qSxMFOh_4E_jWu2oFb7k563jvpIeSewYgxyJ7Ws8WIA09GccKkAH5BBixNZZQJwS_JAICpKEtScU1uvN8BgBSJHJDfZ8SOzlG7tmq_orH3le-xoO-o62hdNUg_dF3T1TYQdNU79J4uUPu9wwbbntqWTraV-caWvjSf7mDpNJA93fhwjq46NL3TNX22ja5auuz6yoR1YrfosDVI17axX05328MtuSp17fHu3Idk8_qynkyj-fJtNhnPI8Nk2kfCgIQylFEJgCiyDJCHOUmZBKMDowQqU8S8LFRsCpAyiVUs0vKzxDJR8ZA8nu52zv7s0ff5zu5dG17mMWRKKSY5BApOlHHWe4dl3rmq0e6QM8iPwvMgPD8Kz8_CQ-ThFKkQ8R-eCsU5i_8A4a99Sw</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Song, Baihang</creator><creator>Jiang, Huiwen</creator><creator>Liu, Jian</creator><creator>Yu, Yao</creator><creator>Luan, Jingmin</creator><creator>Zhao, Yuqian</creator><creator>Wang, Yi</creator><creator>Zhang, Jingyuan</creator><creator>Liu, Zhao</creator><creator>Zhang, Ning</creator><creator>Zhu, Xin</creator><creator>Ma, Zhenhe</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0009-0005-6437-9560</orcidid><orcidid>https://orcid.org/0000-0003-4079-9550</orcidid><orcidid>https://orcid.org/0000-0002-4376-0806</orcidid><orcidid>https://orcid.org/0000-0002-9754-5597</orcidid><orcidid>https://orcid.org/0009-0003-2954-0350</orcidid><orcidid>https://orcid.org/0009-0003-7612-4982</orcidid><orcidid>https://orcid.org/0009-0000-4278-6169</orcidid><orcidid>https://orcid.org/0000-0001-5904-6415</orcidid><orcidid>https://orcid.org/0000-0003-1476-0732</orcidid><orcidid>https://orcid.org/0000-0002-7933-055X</orcidid><orcidid>https://orcid.org/0009-0000-8454-9436</orcidid><orcidid>https://orcid.org/0009-0003-7477-8704</orcidid></search><sort><creationdate>2024</creationdate><title>Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography</title><author>Song, Baihang ; Jiang, Huiwen ; Liu, Jian ; Yu, Yao ; Luan, Jingmin ; Zhao, Yuqian ; Wang, Yi ; Zhang, Jingyuan ; Liu, Zhao ; Zhang, Ning ; Zhu, Xin ; Ma, Zhenhe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c175t-6c070ffffc84006d990e2c8445170ca17586e8cd32fd83cd077438365fbfef483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Biomechanics</topic><topic>Biomedical measurement</topic><topic>Blood</topic><topic>Blood flow</topic><topic>Cardiovascular disease</topic><topic>Congenital diseases</topic><topic>Convolutional neural network (CNN)</topic><topic>Deep learning</topic><topic>Defects</topic><topic>Diseases</topic><topic>Embryo</topic><topic>Embryos</topic><topic>Flow velocity</topic><topic>Heart</topic><topic>Heart diseases</topic><topic>Image segmentation</topic><topic>Localization</topic><topic>Location awareness</topic><topic>Morphogenesis</topic><topic>object detection</topic><topic>Optical Coherence Tomography</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>semantic segmentation</topic><topic>Shear stress</topic><topic>spectral-domain optical coherence tomography (SD-OCT)</topic><topic>Stress measurement</topic><topic>Time measurement</topic><topic>Tomography</topic><topic>wall shear stress (WSS)</topic><topic>Wall shear stresses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Baihang</creatorcontrib><creatorcontrib>Jiang, Huiwen</creatorcontrib><creatorcontrib>Liu, Jian</creatorcontrib><creatorcontrib>Yu, Yao</creatorcontrib><creatorcontrib>Luan, Jingmin</creatorcontrib><creatorcontrib>Zhao, Yuqian</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Zhang, Jingyuan</creatorcontrib><creatorcontrib>Liu, Zhao</creatorcontrib><creatorcontrib>Zhang, Ning</creatorcontrib><creatorcontrib>Zhu, Xin</creatorcontrib><creatorcontrib>Ma, Zhenhe</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Song, Baihang</au><au>Jiang, Huiwen</au><au>Liu, Jian</au><au>Yu, Yao</au><au>Luan, Jingmin</au><au>Zhao, Yuqian</au><au>Wang, Yi</au><au>Zhang, Jingyuan</au><au>Liu, Zhao</au><au>Zhang, Ning</au><au>Zhu, Xin</au><au>Ma, Zhenhe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2024</date><risdate>2024</risdate><volume>73</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>Congenital heart disease, the most common birth defect in newborns and children, highlights the significance of understanding heart development. In the early development stage, the biomechanical environment, especially wall shear stress (WSS), plays a crucial role in heart morphogenesis. The outflow tract (OFT) is an important segment of the embryonic heart, and a large portion of congenital heart defects originate in the OFT. However, real-time measurement of WSS in the OFT of animal models remains challenging. We propose an automatic-localization segmentation network (ALSegNet) integrated with spectral domain optical coherence tomography (SD-OCT) to achieve real-time WSS calculation. Our ALSegNet accurately extracts the blood flow area from the SD-OCT structure image by incorporating an automatic localization module. WSS calculation is performed with the combination of extracted flow area and flow velocity information provided by SD-OCT. The network achieves segmentation within 22.2 ms, making the total WSS calculation time approximately 36 ms for each B-scan. Our approach achieves a speed of 27 frames/s, adequate for video display. Using the developed system, we successfully monitor in vivo WSS in the OFT of chicken embryos, providing valuable insights into embryonic heart development. The integration of deep learning with SD-OCT enables real-time and accurate WSS measurements, offering a powerful tool for studying congenital heart disease and heart morphogenesis.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIM.2024.3417602</doi><tpages>11</tpages><orcidid>https://orcid.org/0009-0005-6437-9560</orcidid><orcidid>https://orcid.org/0000-0003-4079-9550</orcidid><orcidid>https://orcid.org/0000-0002-4376-0806</orcidid><orcidid>https://orcid.org/0000-0002-9754-5597</orcidid><orcidid>https://orcid.org/0009-0003-2954-0350</orcidid><orcidid>https://orcid.org/0009-0003-7612-4982</orcidid><orcidid>https://orcid.org/0009-0000-4278-6169</orcidid><orcidid>https://orcid.org/0000-0001-5904-6415</orcidid><orcidid>https://orcid.org/0000-0003-1476-0732</orcidid><orcidid>https://orcid.org/0000-0002-7933-055X</orcidid><orcidid>https://orcid.org/0009-0000-8454-9436</orcidid><orcidid>https://orcid.org/0009-0003-7477-8704</orcidid></addata></record> |
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| subjects | Biomechanics Biomedical measurement Blood Blood flow Cardiovascular disease Congenital diseases Convolutional neural network (CNN) Deep learning Defects Diseases Embryo Embryos Flow velocity Heart Heart diseases Image segmentation Localization Location awareness Morphogenesis object detection Optical Coherence Tomography Real time Real-time systems semantic segmentation Shear stress spectral-domain optical coherence tomography (SD-OCT) Stress measurement Time measurement Tomography wall shear stress (WSS) Wall shear stresses |
| title | Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography |
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