Clinical Validation of Atlas-Based Auto-Segmentation of Multiple Target Volumes and Normal Tissue (Swallowing/Mastication) Structures in the Head and Neck

Teguh, David N.; Levendag, Peter C.; Voet, P.; Al‐Mamgani, Abrahim; Han, Xiao; Wolf, Theresa K.; Hibbard, Lyndon S.; Nowak, Peter; Akhiat, Hafid; Dirkx, M.; Heijmen, B.; Hoogeman, M.

doi:10.1016/j.ijrobp.2010.07.009

Cited by 166 publications

(176 citation statements)

References 18 publications

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“…Unsupervised contour propagation in large datasets of daily imaging could enable a comparison of delivered and planned doses for the OARs described in this article, excluding the esophagus and esophagus inlet muscle. Previous studies have already described the benefits of DIR for the purpose of reducing contouring time during treatment [15,16,26]. Another application of the method might be replacing any CT being performed during treatment for adaptive purposes.…”

Section: Discussionmentioning

confidence: 99%

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Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy

et al. 2016

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Background: Autocontouring improves workflow in computed tomography (CT)-based dose planning, but could also potentially play a role for optimal use of daily cone beam CT (CBCT) in adaptive radiotherapy. This study aims to determine the accuracy of a deformable image registration (DIR) algorithm for organs at risk (OAR) in the neck region, when applied to CBCT. Material and methods: For 30 head and neck cancer (HNC) patients 14 OARs including parotid glands, swallowing structures and spinal cord were delineated. Contours were propagated by DIR from CT to the CBCTs of the first and last treatment fraction. An indirect approach, propagating contours to the first CBCT and from there to the last CBCT was also tested. Propagated contours were compared to manually corrected contours by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dose was recalculated on CBCTs and dosimetric consequences of uncertainties in DIR were reviewed. Results: Mean DSC values of !0.8 were considered adequate and were achieved in tongue base (0.91), esophagus (0.85), glottic (0.81) and supraglottic larynx (0.83), inferior pharyngeal constrictor muscle (0.84), spinal cord (0.89) and all salivary glands in the first CBCT. For the last CBCT by direct propagation, adequate DSC values were achieved for tongue base (0.85), esophagus (0.84), spinal cord (0.87) and all salivary glands. Using indirect propagation only tongue base (0.80) and parotid glands (0.87) were !0.8. Mean relative dose difference between automated and corrected contours was within ±2.5% of planed dose except for esophagus inlet (-4.5%) and esophagus (5.0%) for the last CBCT using indirect propagation. Conclusion: Compared to manually corrected contours, the DIR algorithm was accurate for use in CBCT images of HNC patients and the minor inaccuracies had little consequence for mean dose in most clinically relevant OAR. The method can thus enable a more automated segmentation of CBCT for use in adaptive radiotherapy.

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Section: Discussionmentioning

confidence: 99%

“…There is a growing array of commercial software aimed at easing this burden by automatic contouring, utilizing novel deformable image registration (DIR) techniques to propagate one set of contours from an initial CT to fit the anatomy of a second CT [13,14]. Such techniques have been found to reduce contouring time considerably [15,16].…”

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confidence: 99%

Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy

et al. 2016

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“…Mostly, this is performed manually on two-dimensional slices by experts and is time consuming. Wider use of adaptive radiotherapy (ART) has increased the need for automated segmentation algorithms [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…How should these agreements and differences amongst expert outlines be used for quantitative validation? The Dice similarity coefficient (DSC) [7], the ratio of the volume of intersection of two segmentations to their mean volume, is a commonly used metric for the validation of automated segmentation algorithms in radiotherapy [1][2][3][4][5][6]. Often, in the case of multiple outlines, the DSC is calculated for an ROI (i.e., the segmentation) and each expert outline individually.…”

Section: Introductionmentioning

confidence: 99%

The Validation Index: A New Metric for Validation of Segmentation Algorithms Using Two or More Expert Outlines With Application to Radiotherapy Planning

Juneja¹,

Evans

Harris³

2013

IEEE Trans. Med. Imaging

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Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses the experts' level of agreement was developed. A control parameter, , was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealised cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSC pair-wise ) and found to be more sensitive than the DSC pair-wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.

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“…International or institutional guidelines, contouring atlases, case libraries and numerous recommendations have thus recently been developed [2][3][4][5][6][7][8][9][10][11][12]. Atlases and guidelines are widely recognized and contribute to reducing inter-observer variability, but they are static documents that also lack interactivity.…”

mentioning

confidence: 99%

Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

Valentini

Boldrini

Damiani

et al. 2014

Radiotherapy and Oncology

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Along with the improved treatment conformity achieved with the recently implemented radiotherapy (RT) planning and delivery approaches [1], there is growing awareness of the uncertainties connected to the definition and delineation of the RT targets as well as the organs at risk (ORs). To assure accurate reproducibility of the planned treatment in order to avoid 'geographical miss' of the target, it is mandatory to correctly identify the volumes to be irradiated and to appropriately manage these uncertainties in the definition of the gross target volume (GTV) as well as of standardized, disease-specific, nodal clinical target volumes (CTVs).Accurate segmentation of primarily the GTVs and CTVs, as well as the ORs, therefore represents the foundation for successful RT. International or institutional guidelines, contouring atlases, case libraries and numerous recommendations have thus recently been developed [2][3][4][5][6][7][8][9][10][11][12]. Atlases and guidelines are widely recognized and contribute to reducing inter-observer variability, but they are static documents that also lack interactivity.To address this challenge, several commercial auto-contouring software solutions have recently been released, representing an opportunity for individualizing the existing atlases, automatically propagating clinically reliable contours to patients' specific anatomies [13]. They also have potential for lowering the segmentation time, increasing the adherence to existing guidelines and, not the least, to reduce the inter-observer variability that still may be a major source of uncertainty in RT. The current issue presents several studies related to the use of such software in RT [14][15][16].A basic requirement of the application of auto-segmentation software is the existence of a widely recognized and reliable definition of the volumes, according to the anatomical region, the histology as well as the stage of the disease. This can be described as software ontology [17], covering clinical, anatomical, pathological and imaging information. This ontology should be linked to benchmark performance values obtained through comparisons with the agreement between multiple observers/operators, or between manual delineations and auto-contouring. There are several metrics for the agreement between the contours, and establishing ranges indicating clinically acceptable agreement on the scales of these metrics plays an essential role in translating the obtained observations to everyday practice.In this paper we would like to share comments and criticisms on how evidence can be derived from the use of auto-segmentation software, addressing the key aspects that should be considered in future studies in this field: ontology definition, benchmark evaluation methods and performance evaluation tools. We also discuss the potential benefits that can be achieved with these tools. Ontology definitionThe term ontology refers to a form of dictionary where information is specified and organized in a well-defined semantic data collection model; a se...

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Clinical Validation of Atlas-Based Auto-Segmentation of Multiple Target Volumes and Normal Tissue (Swallowing/Mastication) Structures in the Head and Neck

Cited by 166 publications

References 18 publications

Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy

Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy

The Validation Index: A New Metric for Validation of Segmentation Algorithms Using Two or More Expert Outlines With Application to Radiotherapy Planning

Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

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