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Smith AG, Petersen J, Terrones-Campos C, Berthelsen AK, Forbes NJ, Darkner S, Specht L, Vogelius IR. RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy. Med Phys 2021; 49:461-473. [PMID: 34783028 DOI: 10.1002/mp.15353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 09/22/2021] [Accepted: 10/28/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. METHODS We implement an open-source interactive-machine-learning software application that facilitates corrective-annotation for deep-learning generated contours on X-ray CT images. A trained-physician contoured 933 hearts using our software by delineating the first image, starting model training, and then correcting the model predictions for all subsequent images. These corrections were added into the training data, which was used for continuously training the assisting model. From the 933 hearts, the same physician also contoured the first 10 and last 10 in Eclipse (Varian) to enable comparison in terms of accuracy and duration. RESULTS We find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods. After 923 images had been delineated, hearts took 2 min and 2 s to delineate on average, which includes time to evaluate the initial model prediction and assign the needed corrections, compared to 7 min and 1 s when delineating manually. CONCLUSIONS Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows.
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Affiliation(s)
- Abraham George Smith
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jens Petersen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Cynthia Terrones-Campos
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anne Kiil Berthelsen
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Department of Clinical Physiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Nora Jarrett Forbes
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Lena Specht
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Richter Vogelius
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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