Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data

M. van Stralen, J.G. Bosch, M.M. Voormolen, G. van Burken, B.J. Krenning, R.J.M. van Geuns, E. Angelié, R.J. van der Geest, C.T. Lancee, N. de Jong, J.H.C. Reiber

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)

Abstract

We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements.
Original languageEnglish
Title of host publicationProceedings Medical Imaging 2005: Image Processing
EditorsJ. Michael Fitzpatrick, Joseph M. Reinhardt
Place of PublicationSan Diego, CA
PublisherSPIE
Pages1457-1467
Number of pages11
DOIs
Publication statusPublished - 11 Feb 2005
EventMedical Imaging 2005: Image Processing - San Diego, United States
Duration: 11 Feb 200515 Feb 2005

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume5747
ISSN (Print)0277-786X

Conference

ConferenceMedical Imaging 2005
CountryUnited States
CitySan Diego
Period11/02/0515/02/05

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Ultrasonics
Magnetic resonance imaging
Pattern matching
Dynamic programming
Transducers
Image quality
Time series

Cite this

van Stralen, M., Bosch, J. G., Voormolen, M. M., van Burken, G., Krenning, B. J., van Geuns, R. J. M., ... Reiber, J. H. C. (2005). Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data. In J. M. Fitzpatrick, & J. M. Reinhardt (Eds.), Proceedings Medical Imaging 2005: Image Processing (pp. 1457-1467). (Proceedings of SPIE; Vol. 5747). San Diego, CA: SPIE. https://doi.org/10.1117/12.596876
van Stralen, M. ; Bosch, J.G. ; Voormolen, M.M. ; van Burken, G. ; Krenning, B.J. ; van Geuns, R.J.M. ; Angelié, E. ; van der Geest, R.J. ; Lancee, C.T. ; de Jong, N. ; Reiber, J.H.C. / Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data. Proceedings Medical Imaging 2005: Image Processing. editor / J. Michael Fitzpatrick ; Joseph M. Reinhardt. San Diego, CA : SPIE, 2005. pp. 1457-1467 (Proceedings of SPIE).
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title = "Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data",
abstract = "We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements.",
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van Stralen, M, Bosch, JG, Voormolen, MM, van Burken, G, Krenning, BJ, van Geuns, RJM, Angelié, E, van der Geest, RJ, Lancee, CT, de Jong, N & Reiber, JHC 2005, Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data. in JM Fitzpatrick & JM Reinhardt (eds), Proceedings Medical Imaging 2005: Image Processing. Proceedings of SPIE, vol. 5747, SPIE, San Diego, CA, pp. 1457-1467, Medical Imaging 2005, San Diego, United States, 11/02/05. https://doi.org/10.1117/12.596876

Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data. / van Stralen, M.; Bosch, J.G.; Voormolen, M.M.; van Burken, G.; Krenning, B.J.; van Geuns, R.J.M.; Angelié, E.; van der Geest, R.J.; Lancee, C.T.; de Jong, N.; Reiber, J.H.C.

Proceedings Medical Imaging 2005: Image Processing. ed. / J. Michael Fitzpatrick; Joseph M. Reinhardt. San Diego, CA : SPIE, 2005. p. 1457-1467 (Proceedings of SPIE; Vol. 5747).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Krenning, B.J.

AU - van Geuns, R.J.M.

AU - Angelié, E.

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N2 - We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements.

AB - We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements.

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van Stralen M, Bosch JG, Voormolen MM, van Burken G, Krenning BJ, van Geuns RJM et al. Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data. In Fitzpatrick JM, Reinhardt JM, editors, Proceedings Medical Imaging 2005: Image Processing. San Diego, CA: SPIE. 2005. p. 1457-1467. (Proceedings of SPIE). https://doi.org/10.1117/12.596876