Combined fuzzy logic and random walker algorithm for PET image tumor delineation

Soufi, Motahare; Kamali-Asl, Alireza; Geramifar, Parham; Abdoli, Mehrsima; Rahmim, Arman

doi:10.1097/mnm.0000000000000428

Cited by 4 publications

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“…Most published studies used manual contouring performed by experienced physicians as the ground truth. 50 However, manual contouring is prone to large interobserver and intraobserver variabilities and, as such, is questionable as reference for assessment of image segmentation techniques. The contouring variability was assessed quantitatively in multicenter studies reporting a significant interobserver variability with a generalized DSC within the range 0.40-0.80.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

2016

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“…TL accuracy quantifies the fraction of times there was any overlap between the predicted and ground-truth segmentations. The HD quantifies shape similarity between the delineated and true tumor boundaries (Foster et al 2014, Soufi et al 2016, with lower values indicating higher shape similarity. HD was computed only for correct TL to quantify shape similarity without the effect of localization errors.…”

Section: Evaluating the Proposed Frameworkmentioning

confidence: 99%

“…The framework was comprehensively evaluated via multiple experiments with independent training and test sets (figure 1(e)). The framework's accuracy on quantifying tumor segmentation and localization in image slices was quantified using Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), true positive fraction, true negative fraction, Hausdorff distance (HD) (Foster et al 2014, Soufi et al 2016, and tumor localization (TL) accuracy. DSC and JSC measure the spatial overlap between the delineated and true tumor masks.…”

Section: Evaluating the Proposed Frameworkmentioning

confidence: 99%

“…To address these issues, several computer-aided segmentation methods for PET images have been developed (Foster et al 2014). Common methods include approaches based on thresholding (Foster et al 2014, Mena et al 2017a, Shah et al 2012, Mena et al 2017b, Sridhar et al 2014, gradient-detection (Werner-Wasik et al 2012), and using image-data statistics or assuming prior knowledge about the tumor (Foster et al 2014, Aristophanous et al 2007, Hatt et al 2009, Jha et al 2010, Soufi et al 2016, Layer et al 2015, Belhassen and Zaidi 2010. Those methods have demonstrated promise but suffer from limitations, such as requiring manual input (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

et al. 2020

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An important need exists for reliable PET tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. The purpose of this study was to develop an automated physics-guided deeplearning-based PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical training data with ground-truth tumor boundaries in PET imaging. We propose a three-module PET-segmentation framework in the context of segmenting primary tumors in 3D 18 F-fluorodeoxyglucose (FDG)-PET images of patients with lung cancer on a per-slice basis. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework's accuracy, generalizability to different scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing the number of training images were quantitatively evaluated using Dice similarity coefficient (DSC) and several other metrics. The framework yielded reliable performance in both simulated (DSC: 0.87 (95% CI: 0.86, 0.88)) and patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely used semi-automated approaches, accurately segmented relatively small tumors (smallest segmented cross-section was 1.83 cm 2 ), generalized across five PET scanners (DSC: 0.74 (95% CI: 0.71, 0.76)), was relatively unaffected by PVEs, and required low training data (training with data from even 30 patients yielded DSC of 0.70 (95% CI: 0.68, 0.71)). A modular deep-learning-based framework yielded reliable automated tumor delineation in FDG-PET images of patients with lung cancer using a small-sized clinical training dataset, generalized across scanners, and demonstrated ability to segment small tumors.

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A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

et al. 2016

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Combined fuzzy logic and random walker algorithm for PET image tumor delineation

Cited by 4 publications

References 35 publications

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

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

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

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