Iterative threshold segmentation for PET target volume delineation

Drever, Laura; Roa, Wilson; McEwan, Alexander; Robinson, Don

doi:10.1118/1.2712043

Cited by 85 publications

(73 citation statements)

References 23 publications

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“…We first considered a phantom including spheres as often used to characterize the performance of tumor segmentation methods (1,5,7,29) or SUV estimation methods (17,30). However, tumors are rarely spheric, and the activity distribution in tissues is far more complex in patients than in phantoms.…”

Section: Discussionmentioning

confidence: 99%

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET

Tylski

Stute²,

Grotus³

et al. 2010

J Nucl Med

133

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In 18 F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from 18 F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations. Methods: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient 18 F-FDG PET scan. Four threshold-based methods (including one, T bgd , accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV max ). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized. Results: In the phantom and simulated data, the T bgd and Fit methods yielded the most accurate volume estimates, with mean errors of 2% 6 11% and 28% 6 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between 231% and 246%, much larger than the bias observed with SUV max (211% 6 23%) or with the PVE-corrected SUV based on T bgd and Fit (22% 6 10% and 3% 6 24%). Conclusion: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T bgd and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T bgd and Fit were more accurate and reproducible than SUV max .

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Section: Discussionmentioning

confidence: 99%

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET

Tylski

Stute²,

Grotus³

et al. 2010

J Nucl Med

133

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“…Drever et al [38] proposed another iterative threshold segmentation method for PET target volume delineation. A phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast between target and background.…”

Section: Iterative Thresholding Methodsmentioning

confidence: 99%

“…The method is based on a convolution of the point-spread function and a sphere with a certain diameter. Phantom data validated that the theoretically optimal RTL depends on the sphere size: RTL=40% (D=15-60 mm), and RTL>50% for small spheres (D<12 mm).Drever et al [38] proposed another iterative threshold segmentation method for PET target volume delineation. A phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast between target and background.…”

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confidence: 99%

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Dević¹

2013

J Nucl Med Radiat Ther

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IntroductionRadiotherapy treatment planning (RTP) over the last few decades has been based on anatomical targets, namely gross tumor volume (GTV) and from it derived clinical target volume (CTV) as well as the planning target volume (PTV). Owing to its superb spatial reproducibility and the ability to provide the information on electron density (useful for heterogeneity corrections), computed tomography (CT) was, and it still represents, the backbone of modern/high technology radiotherapy treatment planning. The lack of sufficient soft tissue contrast in CT resulted in the incorporation of other imaging methods, such as the magnetic resonance imaging (MRI), into the target definition through the process of image co-registration (sometimes incorrectly termed as image fusion). Whereas the MRI has widely contributed to a better definition of the GTV, [1] which represents an anatomical volume, functional information has not been readily available in the framework of conformal 3D treatment planning and delivery in radiotherapy until recently. In addition, it has been widely accepted that a homogeneous dose delivery to the PTV is the standard of care one should pursue to achieve the best local tumor control.Integration of positron emission tomography (PET) and computed tomography (CT) scanners [2][3][4][5] introduced a new dimension in nuclear medicine by combining functional and anatomical imaging in one machine: the PET/CT scanner. Main advantages of PET/CT scanners are:9 Improved quality of reconstructed PET images with the use of a CT map for attenuation correction of emission PET scans 9 Decrease of about half of the PET acquisition time compared to the previous generation of PET scanners, which used an external radioactive source to acquire transmission scan for attenuation correction. In addition, new faster scintillation materials have also contributed to the shortening of the scanning time 9 The combined medical imaging modality is likely helping management of radiotherapy patients 9 Better understanding of tumor metabolic activity spatial localization by the ability to map the distribution of a specific radiopharmaceutical in co-registered PET/CT images, despite the relatively poor spatial resolution of PET image [6,7].With an increased access to PET/CT information and an apparent appreciation that different sections of the tumor functionally do not behave uniformly, radiation oncologists started to contemplate a change in the traditional concept of uniform dose to the PTV delivery. Instead, a notion of biological targeting and dose painting has come into the play. The first question that comes to our attention is how to define the biological target volumes (BTV) and what do they represent? Ten years after the introduction of the PET/CT scanners into the radiation oncology community, target delineation in radiotherapy treatment planning using FDG-PET/CT scans still has a lot of controversy. The aim of this article is to review the limitations of the current methods for the incorporation of FDG based PET/CT ...

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“…Although there are some interesting examples from this group, such as gradient-based (watershed) methods [4,5] and a multimodal generalisation of level set method [6], they are not as well established nor as frequently cited in current reviews as the methods from the second group, which aim to define the optimal threshold value of the uptake in order to segment a tumour. This second group includes approaches that define the optimal threshold as some fixed uptake value, or a fixed percentage of the maximum uptake value; other more sophisticated approaches determine the optimal threshold as the weighted sum of mean target uptake and mean background uptake, among other tecniques [7][8][9][10][11][12]. Note that methods from the second group define a single optimal threshold value for the whole PET 3-D scan.…”

Section: Figmentioning

confidence: 99%

Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

Kerhet

Small²,

Quon³

et al. 2009

Artificial Intelligence in Medicine

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Abstract. Lung cancer represents the most deadly type of malignancy. In this work we propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scans in order to provide a radiation therapist with a "second reader" opinion about the tumour location. For each PET slice, our system extracts a set of attributes, passes them to a trained Support Vector Machine (SVM), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice. We use this technique to analyse four different PET/CT 3D studies. The system produced fairly accurate segmentation, with Jaccard and Dice's similarity coefficients between 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). Besides the high level of geometric similarity, a significant correlation between the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency between the extracted attributes and optimal thresholds.

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Iterative threshold segmentation for PET target volume delineation

Cited by 85 publications

References 23 publications

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

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Iterative threshold segmentation for PET target volume delineation

Cited by 85 publications

References 23 publications

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in 18F-FDG PET

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in 18F-FDG PET

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

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Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET

Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in ¹⁸F-FDG PET