Volumetric Measurement of Synthetic Lung Nodules with Multi–Detector Row CT: Effect of Various Image Reconstruction Parameters and Segmentation Thresholds on Measurement Accuracy

Goo, Jin Mo; Tongdee, Trongtum; Tongdee, Ranista; Yeo, Kwangjae; Hildebolt, Charles F.; Bae, Kyongtae T.

doi:10.1148/radiol.2353040737

Cited by 141 publications

(115 citation statements)

References 26 publications

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“…Overall interexamination variability is expressed as the upper limit of the 95% CI for the relative differences between scan 1 and 2. a Variability in repeated CT examinations (without manual adjustment). b Variability in repeated CT examinations (after manual adjustments) that segmentation performance and accuracy are influenced by this parameter [15]. Whether it also influences interexamination variability has not been tested.…”

Section: Discussionmentioning

confidence: 99%

A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations

et al. 2008

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A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinationsAbstract We compared interexamination variability of CT lung nodule volumetry with six currently available semi-automated software packages to determine the minimum change needed to detect the growth of solid lung nodules. We had ethics committee approval. To simulate a follow-up examination with zero growth, we performed two low-dose unenhanced CT scans in 20 patients referred for pulmonary metastases. Between examinations, patients got off and on the table. Volumes of all pulmonary nodules were determined on both examinations using six nodule evaluation software packages. Variability (upper limit of the 95% confidence interval of the BlandAltman plot) was calculated for nodules for which segmentation was visually rated as adequate. We evaluated 214 nodules (mean diameter 10.9 mm, range 3.3 mm-30.0 mm). Software packages provided adequate segmentation in 71% to 86% of nodules (p< 0.001). In case of adequate segmentation, variability in volumetry between scans ranged from 16.4% to 22.3% for the various software packages. Variability with five to six software packages was significantly less for nodules ≥8 mm in diameter (range 12.9%-17.1%) than for nodules <8 mm (range 18.5%-25.6%). Segmented volumes of each package were compared to each of the other packages. Systematic volume differences were detected in 11/15 comparisons. This hampers comparison of nodule volumes between software packages.

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Section: Discussionmentioning

confidence: 99%

A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations

et al. 2008

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“…Many studies have utilized various phantom scans for tuning parameters and bench-marking their best possible performance in vivo [167,168,184,187,190,193,244,[290][291][292][293].…”

Section: Validationmentioning

confidence: 99%

“…Factors considered in these studies include nodule characteristics such as size [188,236,290,292,302,303], shape [236,303], and appearance types of solidness [240]; pulmonary deformation due to cardiovascular motion [304] and inspiration [236]; CT reconstruction parameters such as slice thickness [244,290,293,302,303,305], slice intervals [244,290,293,305], field of views [244,290,293,305] algorithm types [168,244,305] kernel types [240,293], tube current time [168,244], and dosage settings [238]; CT scanner vendors [292]; segmentation performance due to choices of threshold parameters in a segmentation algorithm [176,290], segmentation algorithms [168,176,291,306], segmentation algorithms in the same segmentation software package [239], segmentation software packages [241], and versions of a segmentation software package …”

Section: Diagnosis Of Lung Nodules Based On Growth Ratementioning

confidence: 99%

“…Hence, alternative 3D approaches were published for the measurement of the growth rate of small nodules. These volumetric measurements [167,168,170,188,220,235,248,290,[310][311][312][313][314][315][316][317][318] have overcome the former 2D limitations.…”

Section: Diagnosis Of Lung Nodules Based On Growth Ratementioning

confidence: 99%

“…Also, some researchers used a number of commercial packages that have been released by the CT vendors for measuring the volume of pulmonary nodules, and a number of studies have evaluated the accuracy and limitations of these software packages. Since the actual volumes of real pulmonary nodules are unknown, such evaluations usually involve either radiologists/experts as the "gold standard" [235,[314][315][316][317] or synthetic phantoms for which the volumes of the nodules are known [168,290,318]. A general limitation of the majority of the volumetric measurement algorithms is that they are only capable of segmenting solid nodules.…”

Section: Diagnosis Of Lung Nodules Based On Growth Ratementioning

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...

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Elastic phantoms generated by microfluidics technology: Validation of an imaged‐based approach for accurate measurement of the growth rate of lung nodules

El-Baz

Sethu

Gimel’farb

et al. 2011

Biotechnology Journal

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This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low-dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring detected lung nodules includes 3D LDCT data registration, which is a non-rigid technique and involves two steps: (i) global target-to-prototype alignment of one scan to another using the experience gained from a prior appearance model, followed by (ii) local alignment to correct for intricate relative deformations. We propose a new approach for validating the accuracy of our algorithm for elastic lung phantoms constructed with state-of-the-art microfluidics technology and in vivo data. Fabricated from a flexible transparent polymer, i.e. polydimethylsiloxane (PDMS), the phantoms mimic the contractions and expansions of the lung and nodules as seen during normal breathing. The in vivo data in our study had been collected from a small control group of four subjects and a larger test group of 27 subjects with known ground truth (biopsy diagnosis. The growth rate and diagnostic results for both phantoms and in vivo data confirm the high accuracy of our algorithm.

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Volumetric Measurement of Synthetic Lung Nodules with Multi–Detector Row CT: Effect of Various Image Reconstruction Parameters and Segmentation Thresholds on Measurement Accuracy

Abstract: For accurate measurement of lung nodule volume, it is critical to select a section thickness and/or segmentation threshold appropriate for the size of a nodule.

Cited by 141 publications

References 26 publications

A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations

A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations

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

Elastic phantoms generated by microfluidics technology: Validation of an imaged‐based approach for accurate measurement of the growth rate of lung nodules

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