Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system

Wormanns, Dag; Fiebich, Martin; Saidi, Mustafa; Diederich, Stefan; Heindel, Walter

doi:10.1007/s003300101126

Cited by 131 publications

(88 citation statements)

References 9 publications

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“…CAD schemes for lung nodule detection were developed first for chest radiographs [6] and then for thick-section CT images [7][8][9][10][11][12][13]. The typical performance of current CAD schemes in thick-section CT is an 80-90% sensitivity with 1-2 false positives per section, which translates into tens of false positives per CT scan.…”

Section: Introductionmentioning

confidence: 99%

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Li¹,

Li²,

Doi³

2008

Academic Radiology

133

105

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We have been developing a computer-aided diagnostic (CAD) scheme for lung nodule detection in order to assist radiologists in the detection of lung cancer in thin-section computed tomography (CT) images. Our database consisted of 117 thin-section CT scans with 153 nodules, obtained from a lung cancer screening program at a Japanese university (85 scans, 91 nodules) and from clinical work at an American university (32 scans, 62 nodules). The database included nodules of different sizes (4-28 mm, mean 10.2 mm), shapes, and patterns (solid and ground-glass opacity (GGO)). Our CAD scheme consisted of modules for lung segmentation, selective nodule enhancement, initial nodule detection, feature extraction, and classification. The selective nodule enhancement filter was a key technique for significant enhancement of nodules and suppression of normal anatomic structures such as blood vessels, which are the main sources of false positives. Use of an automated rule-based classifier for reduction of false positives was another key technique; it resulted in a minimized overtraining effect and an improved classification performance. We employed a case-based four-fold cross-validation testing method for evaluation of the performance levels of our computerized detection scheme. Our CAD scheme achieved an overall sensitivity of 86% (small: 76%, medium-sized: 94%, large: 95%; solid: 86%, mixed GGO: 89%, pure GGO: 81%) with 6.6 false positives per scan; an overall sensitivity of 81% (small: 69%, medium-sized: 91%, large: 91%; solid: 79%, mixed GGO: 88%, pure GGO: 81%) with 3.3 false positives per scan; and an overall sensitivity of 75% (small: 60%, medium-sized: 88%, large: 87%; solid: 70%, mixed GGO: 87%, pure GGO: 81%) with 1.6 false positives per scan. The experimental results indicate that our CAD scheme with its two key techniques can achieve a relatively high performance for nodules presenting large variations in size, shape, and pattern.

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Section: Introductionmentioning

confidence: 99%

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Li¹,

Li²,

Doi³

2008

Academic Radiology

133

105

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“…Many efforts have been made in the development of the computer-aided detection and diagnosis of various abnormalities in medical imaging, for example, for detection and quantification of chronic obstructive pulmonary disease in lung, [12][13][14][15][16] colon cancer, [17][18][19][20] and lesions in mammograms. [21][22][23][24] Computer-aided coronary plaque quantification from CTA has been reported using plaque attenuation thresholds.…”

Section: Introductionmentioning

confidence: 99%

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

et al. 2015

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Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

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“…In our CAD system we adopt a method that uses 3D shape information to identify spherical regions with a given grey level. Following the approach described in [7], the idea is to distinguish spherical from cylindrical (typically blood vessels) shapes analyzing a shape index (SI), defined in terms of 3D characteristics, extracted from sets of voxels with grey level in the range of the nodule intensity. In the past decade, various CAD systems have been proposed for the detection of pulmonary nodules on CT images.…”

Section: Nodule Detectionmentioning

confidence: 99%

Automated CAD for detection of lung nodule using CT scans

Gomathi¹,

Thangaraj²

2010

Proceedings of the Third Annual ACM Bangalore Conference

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The main objective of this paper is to evaluate the performance of the Computer-Aided Detection (CAD) system for automated nodule detection in lungs using CT scan images. The CAD system is applied to CT scans collected in a screening program for lung cancer detection. Each scan consists of a sequence of about 300 slices stored in DICOM (Digital Imaging and Communications in Medicine) format. All true nodules were detected and a very low false-positive detection rate was achieved. The automated extraction of the pulmonary parenchyma in CT images is the most important step in a CAD system. In this paper we describe a method, consisting of techniques which are helpful for the automatic identification of the pulmonary volume. The performance is evaluated as a fully automated computerized method for the detection of lung nodules in computed tomography (CT) scans General Terms-

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Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system

Cited by 131 publications

References 9 publications

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Automated CAD for detection of lung nodule using CT scans

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