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Video Transformer for Segmentation of Echocardiography Images in Myocardial Strain Measurement.

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Title: Video Transformer for Segmentation of Echocardiography Images in Myocardial Strain Measurement.
Authors: Huang, Kuan-Chih1,2 (AUTHOR), Lin, Chang-En3 (AUTHOR), Lin, Donna Shu-Han4 (AUTHOR), Lin, Ting‐Tse3 (AUTHOR), Wu, Cho-Kai3 (AUTHOR), Jeng, Geng-Shi5 (AUTHOR), Lin, Lian-Yu3 (AUTHOR), Lin, Lung-Chun3 (AUTHOR) anniejou@ms28.hinet.net
Source: Journal of Imaging Informatics in Medicine. Jun2026, Vol. 39 Issue 3, p2661-2679. 19p.
Subjects: Left heart ventricle, Computer-assisted image analysis (Medicine), Data analysis, Receiver operating characteristic curves, Research funding, Artificial intelligence, Heart physiology, Ultrasonic imaging, Retrospective studies, Descriptive statistics, Cardiac contraction, Deep learning, Computer-aided diagnosis, Artificial neural networks, Medical records, Acquisition of data, Statistics, Intraclass correlation, Digital image processing, Comparative studies, Data analysis software, Echocardiography, Left ventricular dysfunction, Algorithms
Abstract: The adoption of left ventricular global longitudinal strain (LVGLS) is still restricted by variability among various vendors and observers, despite advancements from tissue Doppler to speckle tracking imaging, machine learning, and, more recently, convolutional neural network (CNN)-based segmentation strain analysis. While CNNs have enabled fully automated strain measurement, they are inherently constrained by restricted receptive fields and a lack of temporal consistency. Transformer-based networks have emerged as a powerful alternative in medical imaging, offering enhanced global attention. Among these, the Video Swin Transformer (V-SwinT) architecture, with its 3D-shifted windows and locality inductive bias, is particularly well suited for ultrasound imaging, providing temporal consistency while optimizing computational efficiency. In this study, we propose the DTHR-SegStrain model based on a V-SwinT backbone. This model incorporates contour regression and utilizes an FCN-style multiscale feature fusion. As a result, it can generate accurate and temporally consistent left ventricle (LV) contours, allowing for direct calculation of myocardial strain without the need for conversion from segmentation to contours or any additional postprocessing. Compared to EchoNet-dynamic and Unity-GLS, DTHR-SegStrain showed greater efficiency, reliability, and validity in LVGLS measurements. Furthermore, the hybridization experiments assessed the interaction between segmentation models and strain algorithms, reinforcing that consistent segmentation contours over time can simplify strain calculations and decrease measurement variability. These findings emphasize the potential of V-SwinT-based frameworks to enhance the standardization and clinical applicability of LVGLS assessments. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Video Transformer for Segmentation of Echocardiography Images in Myocardial Strain Measurement.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Kuan-Chih%22">Huang, Kuan-Chih</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Chang-En%22">Lin, Chang-En</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Donna+Shu-Han%22">Lin, Donna Shu-Han</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Ting‐Tse%22">Lin, Ting‐Tse</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Cho-Kai%22">Wu, Cho-Kai</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeng%2C+Geng-Shi%22">Jeng, Geng-Shi</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Lian-Yu%22">Lin, Lian-Yu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Lung-Chun%22">Lin, Lung-Chun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> anniejou@ms28.hinet.net</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Informatics+in+Medicine%22">Journal of Imaging Informatics in Medicine</searchLink>. Jun2026, Vol. 39 Issue 3, p2661-2679. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Left+heart+ventricle%22">Left heart ventricle</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+physiology%22">Heart physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonic+imaging%22">Ultrasonic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Retrospective+studies%22">Retrospective studies</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiac+contraction%22">Cardiac contraction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+diagnosis%22">Computer-aided diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+records%22">Medical records</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Intraclass+correlation%22">Intraclass correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Echocardiography%22">Echocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Left+ventricular+dysfunction%22">Left ventricular dysfunction</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The adoption of left ventricular global longitudinal strain (LVGLS) is still restricted by variability among various vendors and observers, despite advancements from tissue Doppler to speckle tracking imaging, machine learning, and, more recently, convolutional neural network (CNN)-based segmentation strain analysis. While CNNs have enabled fully automated strain measurement, they are inherently constrained by restricted receptive fields and a lack of temporal consistency. Transformer-based networks have emerged as a powerful alternative in medical imaging, offering enhanced global attention. Among these, the Video Swin Transformer (V-SwinT) architecture, with its 3D-shifted windows and locality inductive bias, is particularly well suited for ultrasound imaging, providing temporal consistency while optimizing computational efficiency. In this study, we propose the DTHR-SegStrain model based on a V-SwinT backbone. This model incorporates contour regression and utilizes an FCN-style multiscale feature fusion. As a result, it can generate accurate and temporally consistent left ventricle (LV) contours, allowing for direct calculation of myocardial strain without the need for conversion from segmentation to contours or any additional postprocessing. Compared to EchoNet-dynamic and Unity-GLS, DTHR-SegStrain showed greater efficiency, reliability, and validity in LVGLS measurements. Furthermore, the hybridization experiments assessed the interaction between segmentation models and strain algorithms, reinforcing that consistent segmentation contours over time can simplify strain calculations and decrease measurement variability. These findings emphasize the potential of V-SwinT-based frameworks to enhance the standardization and clinical applicability of LVGLS assessments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10278-025-01682-5
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      – Code: eng
        Text: English
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        StartPage: 2661
    Subjects:
      – SubjectFull: Left heart ventricle
        Type: general
      – SubjectFull: Computer-assisted image analysis (Medicine)
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Receiver operating characteristic curves
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Heart physiology
        Type: general
      – SubjectFull: Ultrasonic imaging
        Type: general
      – SubjectFull: Retrospective studies
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Cardiac contraction
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Computer-aided diagnosis
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Medical records
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      – SubjectFull: Acquisition of data
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      – SubjectFull: Digital image processing
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      – SubjectFull: Comparative studies
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      – SubjectFull: Data analysis software
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      – SubjectFull: Echocardiography
        Type: general
      – SubjectFull: Left ventricular dysfunction
        Type: general
      – SubjectFull: Algorithms
        Type: general
    Titles:
      – TitleFull: Video Transformer for Segmentation of Echocardiography Images in Myocardial Strain Measurement.
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            NameFull: Huang, Kuan-Chih
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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