Model-Based Analysis of Local Shape for Lesion Detection in CT Scans

Mendonça, Paulo R. S.; Bhotika, Rahul; Sirohey, Saad A.; Turner, Wesley D.; Miller, James V.; Avila, Ricardo S.

doi:10.1007/11566465_85

Cited by 18 publications

(23 citation statements)

References 12 publications

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“…The radiologist may also suffer interference factors such as fatigue, Authors Computational technique(s) Choi and Choi [24], Santos et al [4], Chen et al [27] and Li and Doi [80] Hessian matrix based method El-Baz et al [22] and Le et al [81] Genetic algorithm template matching Cascio et al [26] Stable 3D mass-spring models Soltaninejad, Keshani and Tajeripour [28] k-Nearest Neighbors (k-NN) classifier and active contour Suiyuan and Junfeng [29] Thresholding Awai et al [82] Sieve filter Tanino et al [83] Variable n-quoit filter Riccardi et al [30] 3D fast radial transform Namin et al [32] and Murphy et al [84] Shape index Ozekes, Osman and Ucan [38] 3D template matching Ge et al [45] Adaptive weighted k-means clustering Yamada et al [85] and Kanazawa et al [86] Fuzzy clustering Mekada et al [51] Maximum distance inside a connected component Mao et al [87] Fragmentary window filtering Mendonça et al [88] Curvature tensor Paik et al [89] Statistical shape model Agam and Armato [90] Correlation-based enhancement filters Wang et al [25] and Armato III et al [91] Multiple gray-level thresholding Saita et al [92] 3D labeling method subjectivity of the analysis, images acquired with improper configuration of the equipment and noise. A detailed analysis of the LIDC-IDRI database can help us understand the difficulties encountered during this task.…”

Section: False Positive Reductionmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: False Positive Reductionmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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“…We validate its effectiveness by evaluating the impacts on large-scale colon and lung CAD system performances (879 and 770 volumes respectively). The results are very encouraging and significantly outperform the recent state-of-the-arts [1,2,5,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 72%

“…A variety of drastically different techniques have been proposed for lesion detection. However, most previous work [1,2,5,11,12,15,16,23,29,30] focus on extracting low-level, directly observable surface geometry and volumetric intensity features: as geometric descriptors (mostly curvature based) to describe the degree of satisfying the sphericity polyp shape assumption [11,16], segmentation or geometric protrusion based polyp occupancy measurements [12], fuzzy clustering and deformable model [29], and intensity features (as mean, median, maximum, minimum, etc.) [30] or Hessian statistics for polyp detection.…”

Section: Resultsmentioning

confidence: 99%

“…The other reason is that the previous work only focuses on solitary solid nodules [5,15] to show that curvature may be useful, whereas we train a single classifier P BT n to handle all three types of nodules, under various anatomical contexts [6,23]. Partial solid and ground-glass can have very weak, noisy, and non-informative curvature features.…”

Section: Supervised Probabilistic Voxel Map Labeling ℘ In Roimentioning

confidence: 99%

See 1 more Smart Citation

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Lü

Devarakota²,

Vikal³

et al. 2014

Medical Computer Vision. Large Data in Medical Imaging

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Abstract. Computer aided diagnosis (CAD) of cancerous anatomical structures via 3D medical images has emerged as an intensively studied research area. In this paper, we present a principled three-tiered image feature learning approach to capture task specific and data-driven class discriminative statistics from an annotated image database. It integrates voxel-, instance-, and database-level feature learning, aggregation and parsing. The initial segmentation is proceeded as robust voxel labeling and thresholding. After instance-level spatial aggregation, extracted features can also be flexibly tuned for classifying lesions, or discriminating different subcategories of lesions. We demonstrate the effectiveness in the lung nodule detection task which handles all types of solid, partial-solid, and ground-glass nodules using the same set of learned features. Our hierarchical feature learning framework, which was extensively trained and validated on large-scale multiple site datasets of 879 CT volumes (510 training and 369 validation), achieves superior performance than other state-of-the-art CAD systems. The proposed method is also shown to be applicable for colonic polyp detection, including all polyp morphological subcategories, via 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation).

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“…At the first stage, conformal nodule filtering [80] or unsharp masking [81] can enhance nodules and suppress other structures to separate the candidates from the background by simple thresholding (to improve the separation, background trend is corrected in [82][83][84][85] within image regions of interest) or multiple gray-level thresholding technique [38,86,87]. A series of 3D cylindrical and spherical filters are used to detect small lung nodules from high resolution CT images [88][89][90][91][92]. Circular and semicircular nodule candidates can be detected by template matching [81,93,94].…”

Section: Detection Of Lung Nodulesmentioning

confidence: 99%

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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Model-Based Analysis of Local Shape for Lesion Detection in CT Scans

Cited by 18 publications

References 12 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

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

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