Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting

Zeng, Ziming; Wang, Jue; Tiddeman, Bernard; Zwiggelaar, Reyer

doi:10.1016/j.compbiomed.2013.07.027

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…Partially due to its dependence to SUV, 42% threshold-based method that yielded about as unsatisfactory results as FCM method for SUV= 2. In that context, the use of other recent segmentation methods (37)(38)(39)(40) should be also explored to better evaluate the robustness of the approach. Last, regression without truth methods could also be explored to determine whether they are adapted to assess the accuracy of tumor segmentation methods in PET (41).…”

Section: Discussionmentioning

confidence: 99%

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Vignion‐Dewalle¹,

Betrouni²,

Baillet

et al. 2015

Phys. Med. Biol.

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Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians' manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

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Section: Discussionmentioning

confidence: 99%

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Vignion‐Dewalle¹,

Betrouni²,

Baillet

et al. 2015

Phys. Med. Biol.

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“…The result is further tuned using an alpha mating technique which produces soft segmentation. In Zeng et al (), the proposed method is used to remove noise by applying an improved anisotropic diffusion filter. The segmentation of volume of Interest is done using a hierarchical local and global intensity active surface modeling scheme followed by alpha matting step which helps to provide soft segmentation boundary.…”

Section: Brain Tumour Segmentation Techniques Of Pet Imagementioning

confidence: 99%

Automated brain tumour segmentation techniques— A review

Angulakshmi

Priya

2017

Int J Imaging Syst Tech

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Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article.

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“…A number of advanced automatic segmentation methods, such as variational methods[13], supervised and unsupervised learning[14–16], stochastic modeling[17, 18], have been adopted for tumor segmentation in PET[19, 20]. For example, Zeng et al used an anisotropic diffusion filter to remove image noise and then applied a hierarchical active surface modeling scheme to segment tumors[14]. Geets et al proposed a gradient-based watershed method to segment laryngeal tumors[21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive region-growing with maximum curvature strategy for tumor segmentation in¹⁸F-FDG PET

Tan

Choi

et al. 2017

Phys. Med. Biol.

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Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing (CCRG) algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF = 9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI=0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

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Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting

Cited by 17 publications

References 36 publications

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Automated brain tumour segmentation techniques— A review

Adaptive region-growing with maximum curvature strategy for tumor segmentation in¹⁸F-FDG PET

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Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting

Cited by 17 publications

References 36 publications

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Automated brain tumour segmentation techniques— A review

Adaptive region-growing with maximum curvature strategy for tumor segmentation in18F-FDG PET

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Product

Resources

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Adaptive region-growing with maximum curvature strategy for tumor segmentation in¹⁸F-FDG PET