Quality Assessment of Transperineal Ultrasound Images of the Male Pelvic Region Using Deep Learning

Camps, Saskia; Houben, Tim; Edwards, Christopher L.; Antico, Maria; Dunnhofer, Matteo; Martens, Esther; Baeza, J.A.; Vanneste, Hen; Limbergen, Evert Van; Verhaegen, Frank; Carneiro, Gustavo; Fontanarosa, Davide

doi:10.1109/ultsym.2018.8579839

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Furthermore, at patient set up, the probe must be manually adjusted to ensure both reproducible positioning and adequate target volume coverage. Approaches to assist with probe-positioning are being investigated (Camps et al 2017. Remote probe support and robotic systems are also being developed to optimise probe placement during both patient set up and treatment delivery (Schlosser et al 2012, Sen et al 2017, Su et al 2017a.…”

Section: Ultrasound Methodsmentioning

confidence: 99%

Real-time intrafraction motion monitoring in external beam radiotherapy

et al. 2019

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Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to ‘see what we treat, as we treat’ and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

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Section: Ultrasound Methodsmentioning

confidence: 99%

Real-time intrafraction motion monitoring in external beam radiotherapy

et al. 2019

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Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning

Burgt¹,

Camps²,

Antico

et al. 2021

Applied Sciences

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This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a comprehensive view of the surgical site. The implemented algorithm consisted of a weakly supervised neural network, which was trained on 2D US images of different phantoms mimicking the imaging conditions during knee arthroscopy. Image-based classification was performed and the resulting class activation maps were used to localize the arthroscope. The localization performance was evaluated visually by three expert reviewers and by the calculation of objective metrics. Finally, the algorithm was also tested on a human cadaver knee. The algorithm achieved an average classification accuracy of 88.6% on phantom data and 83.3% on cadaver data. The localization of the arthroscope based on the class activation maps was correct in 92–100% of all true positive classifications for both phantom and cadaver data. These results are relevant because they show feasibility of automatic arthroscope localization in 3D US volumes, which is paramount to combining multiple image modalities that are available during knee arthroscopies.

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Quality Assessment of Transperineal Ultrasound Images of the Male Pelvic Region Using Deep Learning

Cited by 2 publications

References 10 publications

Real-time intrafraction motion monitoring in external beam radiotherapy

Real-time intrafraction motion monitoring in external beam radiotherapy

Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning

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