Hari McGrath1,2, Peichao Li3, Reuben Dorent3, Robert Bradford4,5, Shakeel Saeed5,6,7, Sotirios Bisdas8, Sebastien Ourselin3, Jonathan Shapey3,5,9, Tom Vercauteren3. 1. GKT School of Medical Education, King's College London, London, UK. hari.mcgrath@kcl.ac.uk. 2. School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. hari.mcgrath@kcl.ac.uk. 3. School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. 4. Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, UK. 5. Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK. 6. The Ear Institute, UCL, London, UK. 7. The Royal National Throat Nose and Ear Hospital, London, UK. 8. Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK. 9. Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK.
Abstract
PURPOSE: Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard. METHODS: Using high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy. RESULTS: We found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, [Formula: see text]), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation. CONCLUSION: We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.
PURPOSE: Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard. METHODS: Using high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy. RESULTS: We found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, [Formula: see text]), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation. CONCLUSION: We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.
Authors: J Shapey; K Barkas; S Connor; A Hitchings; H Cheetham; S Thomson; J M U-King-Im; R Beaney; D Jiang; S Barazi; R Obholzer; Nwm Thomas Journal: Ann R Coll Surg Engl Date: 2018-03 Impact factor: 1.891
Authors: Gloria P Mazzara; Robert P Velthuizen; James L Pearlman; Harvey M Greenberg; Henry Wagner Journal: Int J Radiat Oncol Biol Phys Date: 2004-05-01 Impact factor: 7.038
Authors: Bjoern H Menze; Andras Jakab; Stefan Bauer; Jayashree Kalpathy-Cramer; Keyvan Farahani; Justin Kirby; Yuliya Burren; Nicole Porz; Johannes Slotboom; Roland Wiest; Levente Lanczi; Elizabeth Gerstner; Marc-André Weber; Tal Arbel; Brian B Avants; Nicholas Ayache; Patricia Buendia; D Louis Collins; Nicolas Cordier; Jason J Corso; Antonio Criminisi; Tilak Das; Hervé Delingette; Çağatay Demiralp; Christopher R Durst; Michel Dojat; Senan Doyle; Joana Festa; Florence Forbes; Ezequiel Geremia; Ben Glocker; Polina Golland; Xiaotao Guo; Andac Hamamci; Khan M Iftekharuddin; Raj Jena; Nigel M John; Ender Konukoglu; Danial Lashkari; José Antonió Mariz; Raphael Meier; Sérgio Pereira; Doina Precup; Stephen J Price; Tammy Riklin Raviv; Syed M S Reza; Michael Ryan; Duygu Sarikaya; Lawrence Schwartz; Hoo-Chang Shin; Jamie Shotton; Carlos A Silva; Nuno Sousa; Nagesh K Subbanna; Gabor Szekely; Thomas J Taylor; Owen M Thomas; Nicholas J Tustison; Gozde Unal; Flor Vasseur; Max Wintermark; Dong Hye Ye; Liang Zhao; Binsheng Zhao; Darko Zikic; Marcel Prastawa; Mauricio Reyes; Koen Van Leemput Journal: IEEE Trans Med Imaging Date: 2014-12-04 Impact factor: 10.048
Authors: Christopher J Kelly; Alan Karthikesalingam; Mustafa Suleyman; Greg Corrado; Dominic King Journal: BMC Med Date: 2019-10-29 Impact factor: 8.775
Authors: Melissa R Requist; Yantarat Sripanich; Andrew C Peterson; Tim Rolvien; Alexej Barg; Amy L Lenz Journal: Int J Comput Assist Radiol Surg Date: 2021-02-19 Impact factor: 2.924
Authors: Paul Windisch; Carole Koechli; Susanne Rogers; Christina Schröder; Robert Förster; Daniel R Zwahlen; Stephan Bodis Journal: Cancers (Basel) Date: 2022-05-27 Impact factor: 6.575
Authors: Nada Mufti; Michael Ebner; Premal Patel; Michael Aertsen; Trevor Gaunt; Paul D Humphries; Fonteini Emmananouella Bredaki; Richard Hewitt; Colin Butler; Magdalena Sokolska; Giles S Kendall; David Atkinson; Tom Vercauteren; Sebastien Ourselin; Pranav P Pandya; Jan Deprest; Andrew Melbourne; Anna L David Journal: OTO Open Date: 2021-10-25