Purpose The aim of this study was to evaluate the impact of consensus algorithms on segmentation results when applied to clinical petimages. In particular, whether the use of the majority vote or STAPLE algorithm cou...
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Purpose The aim of this study was to evaluate the impact of consensus algorithms on segmentation results when applied to clinical petimages. In particular, whether the use of the majority vote or STAPLE algorithm could improve the accuracy and reproducibility of the segmentation provided by the combination of three semiautomatic segmentation algorithms was investigated. Methods Three published segmentation methods (contrast-oriented, possibility theory and adaptive thresholding) and two consensus algorithms (majority vote and STAPLE) were implemented in a single software platform (ArtiviewA (R)). Four clinical datasets including different locations (thorax, breast, abdomen) or pathologies (primary NSCLC tumours, metastasis, lymphoma) were used to evaluate accuracy and reproducibility of the consensus approach in comparison with pathology as the ground truth or CT as a ground truth surrogate. Results Variability in the performance of the individual segmentation algorithms for lesions of different tumour entities reflected the variability in petimages in terms of resolution, contrast and noise. Independent of location and pathology of the lesion, however, the consensus method resulted in improved accuracy in volume segmentation compared with the worst-performing individual method in the majority of cases and was close to the best-performing method in many cases. In addition, the implementation revealed high reproducibility in the segmentation results with small changes in the respective starting conditions. There were no significant differences in the results with the STAPLE algorithm and the majority vote algorithm. Conclusion This study showed that combining different petsegmentation methods by the use of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendat
Accurate evaluation of functionally significant target volumes in combination with anatomic imaging is of primary importance for effective radiation therapy treatment planning In this study, a method for rapid and acc...
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Accurate evaluation of functionally significant target volumes in combination with anatomic imaging is of primary importance for effective radiation therapy treatment planning In this study, a method for rapid and accurate pet image segmentation and volumetrics based on phantom measurements and independent of scanner calibration was developed A series of spheres ranging in volume from 0 5 mL to 95 mL were imaged in an anthropomorphic phantom of human thorax using two commercial pet and CT/pet scanners The target to background radioactivity concentration ratio ranged from 3 1 to 12 1 in 11 separate phantom scanning experiments The results confirmed that optimal segmentation thresholding depends on target volume and radioactivity concentration ratio This information can be derived from a generalized pre-determined "lookup table" of volume and contrast dependent threshold values instead of using fitted curves derived from machine specific information A three-step method based on the petimage intensity information alone was used to delineate volumes of interest First, a mean intensity segmentation method was used to generate an initial estimate of target volume, and the radioactivity concentration ratio was computed by a family of recovery coefficient curves to compensate for the partial volume effect Next, the appropriate threshold value was obtained from a phantom-generated threshold lookup table Lastly, a threshold level set method was performed on the threshold value to further refine the target contour by reducing the limitation of global thresholding The segmentation results were consistent for spheres greater than 2 5 mL which yielded volume average uncertainty of 11 2% in phantom studies The results of segmented volumes were comparable to those determined by contrast-oriented method and iterative threshold method (ITM) In addition, the new volume segmentation method was applied clinically to ten patients undergoing pet/CT volume analysis for radiation therapy t
Owing to fogged image quality and similar gray scale of pet, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on...
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Owing to fogged image quality and similar gray scale of pet, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on the basis of visual saliency model of petimages. Firstly, manual operation substituted by optimized Itti visual saliency model distinguishes petimages in a fast way. Secondly, one should preprocess the salient images acquired, and then initialize the Gaussian mixture model of foreground and background area. Finally, the pet salient images are segmented by optimized GrabCut algorithm, thus obtaining results. Compared with the other two algorithms, experimental results show that the proposed algorithm has some advantages in the simple operation, the efficient algorithm and the accurate results. At the same time, it effectively improves the efficiency of pet image segmentation and ensures the segmentation results. (C) 2016 The Authors. Published by Elsevier B.V.
segmentation of positron emission tomography (pet) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagn...
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ISBN:
(纸本)9781424441228
segmentation of positron emission tomography (pet) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagnosis. In this study, we present the use of a graph based method for providing robust, accurate, and reliable segmentation of functional volumes on petimages from standardized uptake values (SUVs). We validated the success of the segmentation method on different pet phantoms including ground truth CT simulation, and compared it to two well-known threshold based segmentation methods. Furthermore, we assessed intra- and inter-observer variation in delineation accuracy as well as reproducibility of delineations using real clinical data. Experimental results indicate that the presented segmentation method is superior to the commonly used threshold based methods in terms of accuracy, robustness, repeatability, and computational efficiency.
While accurate tumor delineation in FDG-pet is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a power...
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ISBN:
(纸本)9781509011728
While accurate tumor delineation in FDG-pet is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available methods, pet voxels are described not only by intensities but also complementally by features extracted from patches. Considering there are a large amount of features without consensus regarding the most informative ones, and some of them are even unreliable due to image quality, a specific procedure is adopted to adapt distance metric for properly representing clustering distortions and neighborhood similarities. A specific spatial regularization is also included in the clustering algorithm to effectively quantify local homogeneity. The proposed method has been evaluated by real-patient images, showing good performance.
Owing to fogged image quality and similar gray scale of pet, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on...
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Owing to fogged image quality and similar gray scale of pet, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on the basis of visual saliency model of petimages. Firstly, manual operation substituted by optimized Itti visual saliency model distinguishes petimages in a fast way. Secondly, one should preprocess the salient images acquired, and then initialize the Gaussian mixture model of foreground and background area. Finally, the pet salient images are segmented by optimized GrabCut algorithm, thus obtaining results. Compared with the other two algorithms, experimental results show that the proposed algorithm has some advantages in the simple operation, the efficient algorithm and the accurate results. At the same time, it effectively improves the efficiency of pet image segmentation and ensures the segmentation results.
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