Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation

Schaefer, A.; Vermandel, Maximilien; Baillet, C.; Vignion‐Dewalle, Anne‐Sophie; Modzelewski, Romain; Véra, Pierre; Massoptier, Laurent; Parcq, Caroline; Gibon, D.; Fechter, Tobias; Nemer, U.; Gardin, I.; Nestle, Ursula

doi:10.1007/s00259-015-3239-7

Cited by 37 publications

(37 citation statements)

References 46 publications

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“…Within the learning category would also fall the recent approaches to account for a set or contours generated via multiple automatic methods, through averaging/consensus methods, statistical methods such as the “inverse–ROC (receiver operating characteristic)” approach, STAPLE (simultaneous truth and performance level estimation)‐derived methods, majority voting, or decision tree to generate a surrogate of truth. Most of these methods would need some type of “training” or preliminary determination of parameters for the particular type of lesions and may therefore avoid the limitations of the individual methods used.…”

Section: Description and Classification Of The Algorithmsmentioning

confidence: 99%

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Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

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Section: Description and Classification Of The Algorithmsmentioning

confidence: 99%

“…At present, there is not a sufficient amount of published data to give specific recommendations for each clinical site. The emerging consensus and decision tree based methods, however, provide a potential to provide adequate solution for each site if appropriately adapted and trained.…”

Section: Comparison Of the Pet‐as Algorithms Based On Current Publicamentioning

confidence: 99%

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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“…In RT, PET image registration is used for the following purposes among others: (a) better tumor target definition; (b) propagation of contours from one image set to another; (c) adaptive therapy planning/dose painting to selectively increase dose to more PET‐avid target regions; and (d) incorporation of respiratory‐gated PET information in treatment planning . The reader is referred to AAPM Task Group 132 (TG‐132: Use of Image Registration and Fusion Algorithms and Techniques in Radiotherapy), which deals with the topic of image registration in RT.…”

Section: Current Usage Of [18f]fdg‐pet In Rtmentioning

confidence: 99%

Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy

et al. 2019

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The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18F‐fluorodeoxyglucose ([18F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18F]FDG‐PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18F]FDG‐PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18F]FDG‐PET for RT.

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“…43 The clinical assessment of the LU database (ten studies) resulted in a mean DSC of 0.70 and mean CE of 56.21%, compared with 0.41 and 112.86% 43 and 0.54 and 60.58% 34 (nine studies), respectively. Schaefer et al 3 evaluated consensus contours from different PET segmentation algorithms using clinical PET images and reported a mean DSC varying from 0.59 to 0.67.…”

Section: Discussionmentioning

confidence: 99%

“…At the present time, there is no consensus on the best performing algorithm that can be adopted as standard for all or a number of indications. [1][2][3] Previous studies have shown a large variability in terms of computational complexity and amount of user interaction required by the various image segmentation techniques. [4][5][6] Recent reviews of state-of-the-art PET image segmentation techniques indicate that there is no optimal solution for all types of clinical oncology indications with respect to accuracy, precision, and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Generic and robust method for automatic segmentation of PET images using an active contour model

2016

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Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions:The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation. C

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Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation

Cited by 37 publications

References 46 publications

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy

Generic and robust method for automatic segmentation of PET images using an active contour model

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Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation

Cited by 37 publications

References 46 publications

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Task Group 174 Report: Utilization of [18F]Fluorodeoxyglucose Positron Emission Tomography ([18F]FDG‐PET) in Radiation Therapy

Generic and robust method for automatic segmentation of PET images using an active contour model

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Product

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Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy