A Texture-Based Probabilistic Approach for Lung Nodule Segmentation

Zinoveva, Olga; Zinovev, Dmitriy; Siena, Stephen; Raicu, Daniela; Furst, Jacob; Armato, Samuel G.

doi:10.1007/978-3-642-21596-4_3

Cited by 10 publications

(13 citation statements)

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“…Technical approaches previously reported for volumetric lung nodule segmentation can be roughly classified into the following eleven categories: (1) thresholding [140–144, 146, 154], (2) mathematical morphology [73, 76, 147, 152, 153, 158], (3) region growing [152, 153, 175–178], (4) deformable model [137, 138, 160, 161, 163, 168, 182, 255], (5) dynamic programming [145, 169, 180], (6) spherical/ellipsoidal model fitting [148, 149, 151, 256, 257], (7) probabilistic classification [97, 156, 157, 166, 167, 174, 181], (8) discriminative classification [162, 183], (9) mean shift [150, 151, 170], (10) graph-cuts [172, 173], and (11) watersheds [165]. …”

Section: Lung Nodule Segmentationmentioning

confidence: 99%

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

El-Baz

Beache

Gimel’farb

et al. 2013

International Journal of Biomedical Imaging

190

106

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This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

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Section: Lung Nodule Segmentationmentioning

confidence: 99%

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

El-Baz

Beache

Gimel’farb

et al. 2013

International Journal of Biomedical Imaging

190

106

View full text Add to dashboard Cite

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“…Zinoveva et al performed nodule segmentation and addressed the usage of observations with uncertain truth by using probability maps computed using the multiple contours of suspected lung nodules supplied by a panel of radiologists. This was used to create a classifier that determined if a particular pixel belonged to the nodule [7].…”

Section: Related Workmentioning

confidence: 99%

Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

Smith

Stein

Furst

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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Computer-aided diagnosis (CAD) can be used as second readers in the imaging diagnostic process. Typically to create a CAD system, the region of interest (ROI) has to be first detected and then delineated. This can be done either manually or automatically. Given that manually delineating ROIs is a time consuming and costly process, we propose a CAD system based on multiple computer-derived weak segmentations (WSCAD) and show that its diagnosis performance is at least as good as the predictions developed using manual radiologist segmentations. The proposed CAD system extracts a set of image features from the weak segmentations and uses them in an ensemble of classification algorithms to predict semantic ratings such as malignancy. These automated results are compared against a reference truth based on ratings and segmentations provided by radiologists to determine if it is necessary to obtain manual radiologist segmentations in order to develop a CAD. By developing a pair of CADs using the Lung Image Database Consortium (LIDC) data, we show that WSCADs are at least as accurate in predicting semantic ratings as CADs based on radiologist segmentation.

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“…The k-nearest neighbor regression was used to establish this function. Zinoveva et al [217] proposed a similar soft segmentation method by using a decision tree classifier with CART algorithm [232].…”

Section: H Discriminative Classification (Dc) Casts the Segmentationmentioning

confidence: 99%

“…With such GTs, various segmentation methods have been validated by a number of quantitative accuracy and error measures, such as 1) overlap ratio (a fraction of cardinality of the intersection and the union of voxel sets for a lesion's segmentation and its GT) [174,199,203,204,210,[215][216][217]219], 2) percentage voxel error rate (percentage of voxels mis-segmented with respect to the total number of voxels in a nodule) [197,199,204,220], and 3) percentage volume error rate (percentage of error in volume measurement with respect to the GT's volume) [187,193,216]. The mean, standard deviation, and the root-mean-square statistics are often reported for these accuracy/error measures computed for a set of test cases.…”

Section: Validationmentioning

confidence: 99%

“…It was the Lung Image Database Consortium (LIDC) that made the most significant efforts to make such nodule CT datasets with GTs publicly available [158,295]. Currently two datasets covering many type of nodules with multiple GT segmentations for each case are available through their website [296], which have already been used by many studies since 2005 [174,193,199,203,204,216,217,249]. More recently, Italung dataset from a lung cancer screening program in Italy [297,298] has been used in the studies by Diciotti et al [193,249].…”

Section: Validationmentioning

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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A Texture-Based Probabilistic Approach for Lung Nodule Segmentation

Cited by 10 publications

References 13 publications

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

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

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