Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non–small-cell lung cancer?

Fox, Jana; Rengan, Ramesh; O'Meara, W; Yorke, Ellen; Erdi, Yusuf E.; Nehmeh, Sadek; Leibel, Steven A.; Rosenzweig, Kenneth E.

doi:10.1016/j.ijrobp.2004.09.020

Cited by 147 publications

(98 citation statements)

References 18 publications

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“…Lack of physiological information in CT makes it difficult to distinguish between the lesion and the normal tissues because the tumor intensity is similar to the surrounding tissues [5] [see Fig.1(b)]. Many studies indicated that the combination of PET and CT images produce a more consistent tumor volumes [6]- [8]. Therefore, PET-CT gained a lot of attention in both clinic and the field of image processing and has become a standard method for tumor delineation and cancer assessment.…”

Section: Introductionmentioning

confidence: 99%

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Xiang

Zhang

et al. 2015

IEEE Trans. on Image Process.

136

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Abstract-Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in PET images and low contrast in CT images, segmentation of tumor in PET and CT images is a challenging task. In this study, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method are integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph includes two sub-graphs and a special link is constructed, in which one sub-graph is for PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For PET, a downhill cost and a 3D derivative cost is proposed. For CT, a shape penalty cost is integrated into the region and boundary function which helps constrain the tumor location during the segmentation. We validate our algorithm on a dataset which consists of 18 PET-CT images. The experimental results indicate the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph-cut method.Index Terms-Image segmentation, interactive segmentation, graph cut, random walks, prior information, lung tumor, positron emission tomography (PET), computed tomography (CT).

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Section: Introductionmentioning

confidence: 99%

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Xiang

Zhang

et al. 2015

IEEE Trans. on Image Process.

136

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“…Although still present, following target delineation with PET/CT, inter-observer variability is somehow reduced compared to conventional CT-only contouring [45,46]. PET/CT may provide improved therapeutic ratios compared to conventional CT planning.…”

Section: Delineation Of Target and Organs At Riskmentioning

confidence: 99%

Broadening the scope of Image-Guided Radiotherapy (IGRT)

Greco

Ling

2008

Acta Oncologica

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“…These respiratory movements and cardiac actions result in the target motion which creates significant image blur that affects the accuracy of GTV estimation. On the other hand, using CT only implies a large uncertainty in the result of target volume delin-eation, specially in NSCLC [286,287]. Reported cases, in which the GTV delineated based on CT, include abnormalities that appear totally devoid of FDG activity and can safely be removed from the GTV.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

“…Thus, the combination of PET and CT information has been studied in order to improve the target volume definition especially in NSCL and cases with atelectasis. In this regard, the recent studies have shown that the integration of PET information in the treatment planning has significantly reduced the inter-observer contouring variability [286,287].…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

“…Further studies on the rate of recurrence when PET is used showed that only 1 out of 44 patients developed the tumor recurrence [288]. Steenbakkers et al [286] and Fox et al [287] used software fusion methods and analyzed the observer variation in two phases, one with CT only and another one with fused PET/CT. The two studies addressed the issue of inter-observer variation reduction using matched PET and CT and concluded that the PET/CT software fusion is superior to visual fusion.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

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Computational methods for the analysis of functional 4D-CT chest images.

Soliman¹

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DEDICATIONThis dissertation is dedicated to my father's soul.iii ACKNOWLEDGMENTS In the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me. Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients.v The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decisionmaking. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification.This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today's clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered...

show abstract

Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non–small-cell lung cancer?

Cited by 147 publications

References 18 publications

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Broadening the scope of Image-Guided Radiotherapy (IGRT)

Computational methods for the analysis of functional 4D-CT chest images.

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