Automatic Detection and Quantification of Ground-Glass Opacities on High-Resolution CT Using Multiple Neural Networks

Kauczor, Hans‐Ulrich; Heitmann, Kjell; Heußel, Claus Peter; Marwede, Dirk; Uthmann, Thomas; Thelen, M.

doi:10.2214/ajr.175.5.1751329

Cited by 84 publications

(68 citation statements)

References 22 publications

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“…Kauczor et al [10] reported that the density distribution of non-solid nodules lies in the range of [−750 HU, −300 HU]. The density distribution of solid pulmonary nodules lies in the range of [−200 HU, 200 HU] [19].…”

Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

“…The density distribution of solid pulmonary nodules lies in the range of [−200 HU, 200 HU] [19]. Part-solid nodules are the mixture of solid and non-solid tissue component [10]. The density distribution for a sample part-solid pulmonary nodule is given in Fig.…”

Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

“…So, there is an overlap of internal attenuation of part-solid pulmonary nodules with solid and non-solid nodule. Due to the overlap of internal attenuation, intensity-based thresholding is not enough to classify To extract the core of nodule, VOI is thresholded at −800 HU because minimum attenuation for non-solid nodules is −750 HU [10]. The approximate boundary points of the lung nodule are identified using ray casting technique.…”

Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

“…Hence, thresholdbased approach is inadequate to separate it from the background. In the first step of preprocessing of VOI containing non-solid nodule, the lung parenchyma is removed using the threshold of −800 HU which is well below minimum attenuation for non-solid nodule (−750 HU [10]). The connected component analysis is performed to obtain the object containing nodule.…”

Section: B Preprocessing Of Voi Containing Non-solid Nodulesmentioning

confidence: 99%

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A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The wellcircumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/partsolid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

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Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

Section: Classification Of Pulmonary Nodule Into Solid/part-solid Andmentioning

confidence: 99%

Section: B Preprocessing Of Voi Containing Non-solid Nodulesmentioning

confidence: 99%

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A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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“…An accurate and reproducible method allowing monitoring of fibrosis progression on HRCT would be a valuable surrogate marker of disease. Unfortunately, assessment of fibrosis volumes by expert radiologists has been hampered by substantial intra-and interobserver variability, and quantitative CT indices using fractal analysis and global histogram-based methods have not been validated or found helpful in clinical practice thus far [1,4,[19][20][21][22][23][24]. CALIPER is based on a texture-sensitive volumetric analysis that allows automated classification of lung parenchyma according to a database of HRCT volumes of interest validated by radiologists using data from the LTRC [9].…”

Section: Discussionmentioning

confidence: 99%

Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis

Maldonado

Moua

Rajagopalan³

et al. 2013

Eur Respir J

208

139

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Accurate assessment of prognosis in idiopathic pulmonary fibrosis remains elusive due to significant individual radiological and physiological variability. We hypothesised that short-term radiological changes may be predictive of survival.We explored the use of CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating), a novel software tool developed by the Biomedical Imaging Resource Laboratory at the Mayo Clinic Rochester (Rochester, MN, USA) for the analysis and quantification of parenchymal lung abnormalities on high-resolution computed tomography. We assessed baseline and follow-up (time-points 1 and 2, respectively) high-resolution computed tomography scans in 55 selected idiopathic pulmonary fibrosis patients and correlated CALIPER-quantified measurements with expert radiologists' assessments and clinical outcomes.Findings of interval change (mean 289 days) in volume of reticular densities (hazard ratio 1.91, p50.006), total volume of interstitial abnormalities (hazard ratio 1.70, p50.003) and per cent total interstitial abnormalities (hazard ratio 1.52, p50.017) as quantified by CALIPER were predictive of survival after a median follow-up of 2.4 years. Radiologist interpretation of short-term global interstitial lung disease progression, but not specific radiological features, was also predictive of mortality.These data demonstrate the feasibility of quantifying interval short-term changes on high-resolution computed tomography and their possible use as independent predictors of survival in idiopathic pulmonary fibrosis. @ERSpublications Short-term quantified CT changes are predictive of survival in IPF

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Lungs nodule detection framework from computed tomography images using support vector machine

Khan

Nazir

Khan

et al. 2019

Microscopy Res & Technique

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The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

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Automatic Detection and Quantification of Ground-Glass Opacities on High-Resolution CT Using Multiple Neural Networks

Abstract: Automatic segmentation and quantification of ground-glass opacities on high-resolution CT by a neural network are sufficiently accurate to be implemented for the preinterpretation of images in a clinical environment; it is superior to a double-threshold density mask.

Cited by 84 publications

References 22 publications

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis

Lungs nodule detection framework from computed tomography images using support vector machine

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