Abstract
Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. Probe placement and ultrasound image interpretation are manual tasks contingent upon operator skill, leading to interoperator uncertainties that degrade radiotherapy precision. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is combined with an image classifier using a recurrent neural network to generate two sets of predictions in real-time. The first set identifies relevant prostate anatomy visible in the field of view using the classes: outside prostate, prostate periphery, prostate centre. The second set recommends a probe angular adjustment to achieve alignment between the probe and prostate centre with the classes: move left, move right, stop. The algorithm was trained and tested on 9,743 clinical images from 61 treatment sessions across 32 patients. We evaluated classification accuracy against class labels derived from three experienced observers at 2/3 and 3/3 agreement thresholds. For images with unanimous consensus between observers, anatomical classification accuracy was 97.2% and probe adjustment accuracy was 94.9%. The algorithm identified optimal probe alignment within a mean (standard deviation) range of 3.7° (1.2°) from angle labels with full observer consensus, comparable to the 2.8° (2.6°) mean interobserver range. We propose such an algorithm could assist radiotherapy practitioners with limited experience of ultrasound image interpretation by providing effective real-time feedback during patient set-up.
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Acknowledgements
This work was supported by NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research. The study was also supported by Cancer Research UK under Programmes C33589/A19727 and C20892/A23557, and by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (203145Z/16/Z). The study was jointly supervised by Dr Emma J. Harris, Prof. Dean Barratt and Dr. Ester Bonmati. We thank the radiographers of the Royal Marsden Hospital for their clinical support, as well as David Cooper, Martin Lachaine and David Ash at Elekta for their technical support.
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Grimwood, A., McNair, H., Hu, Y., Bonmati, E., Barratt, D., Harris, E.J. (2020). Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_52
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DOI: https://doi.org/10.1007/978-3-030-59716-0_52
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