AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING

S. M. Camps; T. Houben; G. Carneiro; C. Edwards; M. Antico; M. Dunnhofer; E. G. H. J. Martens; J. A. Baeza; B. G. L. Vanneste; E. J. van Limbergen; P. H. N. de With; F. Verhaegen; D. Fontanarosa

doi:10.1016/j.ultrasmedbio.2019.10.027

AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING

S. M. Camps, T. Houben, G. Carneiro, C. Edwards, M. Antico, M. Dunnhofer, E. G. H. J. Martens, J. A. Baeza, B. G. L. Vanneste, E. J. van Limbergen, P. H. N. de With, F. Verhaegen, D. Fontanarosa^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Original language	English
Pages (from-to)	445-454
Number of pages	10
Journal	Ultrasound in Medicine and Biology
Volume	46
Issue number	2
DOIs	https://doi.org/10.1016/j.ultrasmedbio.2019.10.027
Publication status	Published - Feb 2020

Keywords

Transperineal ultrasound imaging
Deep learning
Prostate
Image-guided radiotherapy
Ultrasound
Radiotherapy
EXTERNAL-BEAM RADIOTHERAPY
ONE-CLASS CLASSIFICATION
INTRA-FRACTION MOTION
PROSTATE
GUIDANCE

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Camps, S. M., Houben, T., Carneiro, G., Edwards, C., Antico, M., Dunnhofer, M., Martens, E. G. H. J., Baeza, J. A., Vanneste, B. G. L., van Limbergen, E. J., de With, P. H. N., Verhaegen, F., & Fontanarosa, D. (2020). AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING. Ultrasound in Medicine and Biology, 46(2), 445-454. https://doi.org/10.1016/j.ultrasmedbio.2019.10.027

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title = "AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING",

abstract = "Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.",

keywords = "Transperineal ultrasound imaging, Deep learning, Prostate, Image-guided radiotherapy, Ultrasound, Radiotherapy, EXTERNAL-BEAM RADIOTHERAPY, ONE-CLASS CLASSIFICATION, INTRA-FRACTION MOTION, PROSTATE, GUIDANCE",

author = "Camps, {S. M.} and T. Houben and G. Carneiro and C. Edwards and M. Antico and M. Dunnhofer and Martens, {E. G. H. J.} and Baeza, {J. A.} and Vanneste, {B. G. L.} and {van Limbergen}, {E. J.} and {de With}, {P. H. N.} and F. Verhaegen and D. Fontanarosa",

year = "2020",

month = feb,

doi = "10.1016/j.ultrasmedbio.2019.10.027",

language = "English",

volume = "46",

pages = "445--454",

journal = "Ultrasound in Medicine and Biology",

issn = "0301-5629",

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Camps, SM, Houben, T, Carneiro, G, Edwards, C, Antico, M, Dunnhofer, M, Martens, EGHJ, Baeza, JA, Vanneste, BGL, van Limbergen, EJ, de With, PHN, Verhaegen, F & Fontanarosa, D 2020, 'AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING', Ultrasound in Medicine and Biology, vol. 46, no. 2, pp. 445-454. https://doi.org/10.1016/j.ultrasmedbio.2019.10.027

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T1 - AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING

AU - Camps, S. M.

AU - Houben, T.

AU - Carneiro, G.

AU - Edwards, C.

AU - Antico, M.

AU - Dunnhofer, M.

AU - Martens, E. G. H. J.

AU - Baeza, J. A.

AU - Vanneste, B. G. L.

AU - van Limbergen, E. J.

AU - de With, P. H. N.

AU - Verhaegen, F.

AU - Fontanarosa, D.

PY - 2020/2

Y1 - 2020/2

N2 - Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

AB - Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

KW - Transperineal ultrasound imaging

KW - Deep learning

KW - Prostate

KW - Image-guided radiotherapy

KW - Ultrasound

KW - Radiotherapy

KW - EXTERNAL-BEAM RADIOTHERAPY

KW - ONE-CLASS CLASSIFICATION

KW - INTRA-FRACTION MOTION

KW - PROSTATE

KW - GUIDANCE

U2 - 10.1016/j.ultrasmedbio.2019.10.027

DO - 10.1016/j.ultrasmedbio.2019.10.027

M3 - Article

C2 - 31780240

SN - 0301-5629

VL - 46

SP - 445

EP - 454

JO - Ultrasound in Medicine and Biology

JF - Ultrasound in Medicine and Biology

IS - 2

ER -

AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING

Abstract

Keywords

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