The Role of Regularization in Deformable Image Registration for Head and Neck Adaptive Radiotherapy

Ciardo, D.; Peroni, M.; Riboldi, Marco; Alterio, Daniela; Baroni, Guido; Orecchia, Roberto

doi:10.7785/tcrt.2012.500327

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…We performed registration using both inhale and exhale volume as reference volumes, with the same parameters for all patients included in the study. The registration algorithm we used provides the possibility to regularize the deformation, regulating its amount with a parameter l, as described in (43,44). Few discontinuities were present in the final deformation field also introducing regularization, in particular next to the diaphragm.…”

Section: Discussionmentioning

confidence: 99%

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Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Peroni

Spadea

Riboldi

et al. 2013

Technol Cancer Res Treat

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The aim of this work is to provide insights into multiple metrics clinical validation of deformable image registration and contour propagation methods in 4D lung radiotherapy planning. The following indices were analyzed and compared: Volume Difference (VD), Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV) and Surface Distances (SD). The analysis was performed on three patient datasets, using as reference a ground-truth volume generated by means of Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm from the outlines of five experts. Significant discrepancies in the quality assessment provided by the different metrics in all the examined cases were found. Metrics sensitivity was more evident in presence of image artifacts and particularly for tubular anatomical structures, such as esophagus or spinal cord. Volume Differences did not account for position and DSC exhibited criticalities due to its intrinsic symmetry (i.e. over-and under-estimation of the reference contours cannot be discriminated) and dependency on the total volume of the structure. PPV analysis showed more robust performance, as each voxel concurs to the classification of the propagation, but was not able to detect inclusion of propagated and ground-truth volumes.Mesh distances could interpret the actual shape of the structures, but might report higher mismatches in case of large local differences in the contour surfaces. According to our study, the combination of VD and SD for the validation of contour propagation algorithms in 4D could provide the necessary failure detection accuracy.

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Section: Discussionmentioning

confidence: 99%

“…minimum of the Jacobian determinant). Plastimatch provides the user with analytic regularization, whose amount can be tuned using a parameter l (43,44). Therefore, we tested the contour propagation with and without regularization.…”

Section: Technology In Cancer Research and Treatment Volume 12 Numbermentioning

confidence: 99%

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Peroni

Spadea

Riboldi

et al. 2013

Technol Cancer Res Treat

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“…Most methods now claim to reach accuracy of one voxel and invertible smooth fields, also thanks to the introduction of regularization terms in the optimization process or as final smoothing. [88][89][90] In addition to morphological postprocessing and refinement, atlas-based segmentation will benefit from advanced refinement methods such as level sets and shape models, to overcome mutual weaknesses and improve overall segmentation accuracy.…”

Section: C2 (Multi)atlas-based Segmentationmentioning

confidence: 99%

Vision 20/20: Perspectives on automated image segmentation for radiotherapy

et al. 2014

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Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra-and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

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“…Peroni et al compared a commercial software (MIMvista, MIM Software, Inc., Cleveland, OH) with an open source optimized B-Spline algorithm for H&N contour propagation and reported no significant differences between the two algorithms. 28 Hoffmann et al evaluated the B-spline-based algorithm implemented within VelocityAI. 20 Based on manually delineated distinct points extracted from seven H&N patient CT image pairs, they reported less than 4 mm target registration error in 79% of cases with reported interuser variability (1 SD) of 1.2(1.1) mm in manual point delineation.…”

Section: F Related Workmentioning

confidence: 99%

Deformable image registration based automatic CT‐to‐CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting

et al. 2014

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The Role of Regularization in Deformable Image Registration for Head and Neck Adaptive Radiotherapy

Cited by 11 publications

References 31 publications

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics

Vision 20/20: Perspectives on automated image segmentation for radiotherapy

Deformable image registration based automatic CT‐to‐CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting

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