2020
DOI: 10.1002/mp.13994
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Fast contour propagation for MR‐guided prostate radiotherapy using convolutional neural networks

Abstract: Purpose To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)‐guided prostate external‐beam radiotherapy. Methods Five prostate cancer patients underwent 20 fractions of image‐guided external‐beam radiotherapy on a 1.5 T MR‐Linac system. For each patient, a pretreatment T2‐weighted three‐dimensional (3D) MR imaging (MRI) scan was used to delineate the clinical target volume (CTV) contours. The same scan was repeated during each fraction, with the … Show more

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Cited by 42 publications
(29 citation statements)
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References 43 publications
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“…Since this is still a time-consuming task, typically only a region encompassing the target volume by 2 cm [21] or 3 cm [151] is considered for manual correction due to time constraints. A potential solution overcoming limitations related to DIR, is the use of deep convolutional neural networks that have raised considerable attention for medical image segmentation and recently been applied to in-room MRI data [156][157][158]. However, clinical certification of such algorithms will be an important hurdle to be cleared in the future.…”
Section: Adaptation For Inter-fractional Changesmentioning
confidence: 99%
“…Since this is still a time-consuming task, typically only a region encompassing the target volume by 2 cm [21] or 3 cm [151] is considered for manual correction due to time constraints. A potential solution overcoming limitations related to DIR, is the use of deep convolutional neural networks that have raised considerable attention for medical image segmentation and recently been applied to in-room MRI data [156][157][158]. However, clinical certification of such algorithms will be an important hurdle to be cleared in the future.…”
Section: Adaptation For Inter-fractional Changesmentioning
confidence: 99%
“…Most DL models for auto-contouring were trained so far for the pelvic region providing to generate organ structures based on MR images ( 32 , 34 ). Additionally, DL concepts for contour propagation from simulation images to daily MR have been proposed recently ( 37 ). Since adaptive MRgRT is a novel clinical application, annotated MR data for model training and validation is sparse, thus alternative approaches such as cross-modality learning have been explored ( 38 ).…”
Section: Real-time Mr Auto-segmentationmentioning
confidence: 99%
“…Many studies focused on target delineations [8] reaching mean dice similarity coefficients compared to manual delineations in the range 0.82-0.95 [25][26][27][28][29][30][31]. Automatic delineation of OARs is also a crucial aspect to achieve full online adaptive radiotherapy and to possibly save time to manual contouring.…”
Section: Introductionmentioning
confidence: 99%