Deformable image registration and interobserver variation in contour propagation for radiation therapy planning

Riegel, Adam C.; Antone, J.; Zhang, Honglai; Jain, Prachi; Raince, Jagdeep; Rea, Anthony; Bergamo, A; Kapur, Ajay; Potters, Louis

doi:10.1120/jacmp.v17i3.6110

Cited by 19 publications

(13 citation statements)

References 44 publications

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“…8,9 However, intra-and interobserver inconsistency has been noted due to different preferences and experience among radiation oncologists. 10,11 Typically, daily manual recontouring is not performed because it is time consuming and new anatomical variations may be introduced in the time it takes to delineate the scan. 12 Automatic recontouring algorithms can alleviate these issues, but robust methods are required, because otherwise still time-consuming fallback strategies are needed.…”

Section: Introductionmentioning

confidence: 99%

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

et al. 2019

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PurposeTo develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity‐Modulated Proton Therapy (IMPT) of prostate cancer using software and deep learning.MethodsA three‐dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours.ResultsThe bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity‐based registration.ConclusionThe proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment‐related adverse side effects.

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Section: Introductionmentioning

confidence: 99%

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

et al. 2019

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“…An expert must then validate and, if necessary, correct the delineations. For example, in prostate cancer, a non-parametric or FFD DIR method has been used to propagate the delineation from the planning CT to the per-treatment images (CT or CBCT) [43][44][45][46][47][48]. Compared to rigid registration, DIR improved the accuracy of the organ delineation, achieving an increase in DSC.…”

Section: Multimodal Image Fusion Morphological and Functionalmentioning

confidence: 99%

“…This approach can be used to track the GTV during EBRT treatment for liver cancer when the per-treatment imaging contrast limits the visualization of the tumor. In H&N cancer, intra-patient DIR has been used to propagate the planning contour to the per-treatment CTs [48,[57][58][59] and CBCTs [60][61][62]. Additionally, when propagating the contours from the pretreatment MRI to the end of treatment MRI, DIR provided a contour error similar to the voxel size (2 mm) and a DSC of around 0.8 [63].…”

Section: Multimodal Image Fusion Morphological and Functionalmentioning

confidence: 99%

“…Step 3: the resulting DVFs are used to propagate the 3D planning dose distributions of the population on the anatomical template coordinate system; step 4: a local statistical analysis of dose-effect relationship is performed. variability in prostate and H&N localization [48,131]. However, its contribution is still limited for CT-CBCT DIR of anatomical localization showing large deformations during EBRT.…”

Section: Uncertainties and Perspectives Of Dir In Rtmentioning

confidence: 99%

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Deformable image registration for radiation therapy: principle, methods, applications and evaluation

et al. 2019

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Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformationbased planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of reirradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

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“…Even if all these strategies are the most widespread in the literature, they are, however, time‐consuming and operator‐dependent. Several studies investigated the inter‐ and intra‐observer variability in manual contouring, highlighting the impact on the registration evaluation, and the subsequent need to account for it . Automatic and efficient strategies are therefore effective alternatives to overcome operator‐dependent variability and to decrease the workload in the clinical procedure.…”

Section: The Geometric Accuracy Paradigmmentioning

confidence: 99%

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

et al. 2018

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Over the last few decades, deformable image registration (DIR) has gained popularity in image-guided radiation therapy for a number of applications, such as contour propagation, dose warping, and accumulation. Although this raises promising perspectives for the improvement of treatment outcomes and quality of radiotherapy clinical practice, the variety of proposed DIR algorithms, combined with the lack of an effective quantitative quality control metric of the registration, is slowing the transfer of DIR into the clinical routine. Recently, a task group (AAPM TG132) report was published outlining the essential aspects of DIR for image guidance in radiotherapy. However, an accurate and efficient patient-specific validation is not yet defined, and appropriate metrics should be identified to achieve the definition of both geometric and dosimetric accuracy. In this respect, the use of a dense set of anatomical landmarks, along with additional evaluations on contours or deformation field analysis, are likely to drive patient-specific DIR validation in clinical image-guided radiotherapy applications to account for geometric inaccuracies. Automatic and efficient strategies able to provide spatial information of DIR uncertainties and to evaluate monomodal and multimodal image registration, as well as to describe homogenous and un-contrasted regions are believed to represent the future direction in DIR validation. But especially in the case of DIR applications for dose mapping and accumulation, the need of accurate patient-specific validation is not only limited to the evaluation of geometric accuracy. In fact, the need to account for dosimetric inaccuracies due to DIR represents another important area in the field of adaptive treatments. Different approaches are currently being investigated to quantify the effect of DIR error on dose analysis, mainly relying on clinically relevant dose metrics, or on the study of deformation field properties for a voxel-by-voxel evaluation. However, novel research is required for the definition of dedicated and personalized measures capable to relate the geometric and dosimetric inaccuracies, thus bearing useful information for a safe use of DIR by clinical end users. In this paper we provide insights on DIR results evaluation on a patient-specific basis, facing the issues of both geometric and dosimetric paradigms. Challenges on DIR validation are overviewed and discussed, in order to push preliminary clinical guidelines forward on this fundamental topic and boost the implementation of more robust and reliable patient-specific evaluation metrics.

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Deformable image registration and interobserver variation in contour propagation for radiation therapy planning

Cited by 19 publications

References 44 publications

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

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