Journal of Medical Radiation SciencesEarly View Continuing Professional DevelopmentOpen Access Continuing professional development - Radiation Therapy First published: 08 February 2023 https://doi.org/10.1002/jmrs.655AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and online at www.asmirt.org/news-and-publications/jmrs, as well as published in JMRS – Volume 70, Issue 4 December 2023. Radiation Therapy – Original Article Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy Gibbons E, Hoffmann M, Westhuyzen J, Hodgson A, Chick B, Last A. (2023) J Med Radiat Sci. https://doi.org/10.1002/jmrs.618 How are deep learning (DL) auto-segmentation contours produced on an incoming data set? DL models use deformable image registration to transform contours from a pre-defined library of similar data sets DL models predict contour location using optimised convolution ‘neural’ networks (CNNs) that have been trained to identify complex non-linear spatial relationships within data sets DL models generate contours by estimating spatial position on a data set using the Dice similarity coefficient (DSC) formula DL models require users to roughly contour the incoming data set by hand before the algorithm can fully optimise each organ at risk (OAR) Which of the following is correct in terms of the number of training data sets used for atlas and DL segmentation? Atlas-based auto-segmentation typically uses a larger number of training data sets than DL models If an atlas and DL auto-segmentation model are trained using the same number of data sets, they are likely to output identical contours The larger number of training data sets used in DL segmentation naturally incorporates a more diverse range of patient anatomy than atlas-based methods DL auto-segmentation models typically reach a performance plateau when trained on 10–30 data sets Which DL-based OARs experienced statistically significant time improvements over atlas-based OARs? Spinal Cord, Rectum, Bladder, Left Femoral Head Spinal Cord, Oral Cavity, Left Lung, Oesophagus, Heart Left Parotid, Spinal Cord, Left Lung, Rectum, Bladder, Left Femoral Head Left Parotid, Rectum, Bladder, Left Femoral Head For auto-segmented OAR contours, it is recommended that: All DL generated OARs should be reviewed by an expert clinician prior to clinical use All DL generated OARs are accurate enough to be used clinically without human review Only a specific list of DL generated OARs requires expert review prior to clinical use Only a specific list of atlas generated OARs require expert review prior to clinical use Why can vendor trained ‘generic’ DL models be beneficial to a radiation therapy workflow? They will always output contours that are quantitatively more accurate than a DL model trained by an individual department They are pre-optimised to output contours that will meet the specific contouring protocols for all departments worldwide The output contours will always require less editing time than a DL model trained by an individual department They can output quality contours without the time, data or resource requirements needed for departments to individually train their own DL models Recommended further reading: 1Brouwer CL, Boukerroui D, Oliveira J, et al. Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy. Phys Imaging Radiat Oncol. 2020; 16: 54– 60. 2Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol. 2021; 160: 185– 91. 3van Dijk LV, Van den Bosch L, Aljabar P, et al. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiother Oncol. 2020; 142: 115– 23. Answers Scan this QR code to find the answers, or visit www.asmirt.org/news-and-publications/jmrs Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
Read full abstract