Bartjan de Hoop - Academia.edu (original) (raw)

Papers by Bartjan de Hoop

Research paper thumbnail of Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy

Radiology, Aug 28, 2012

To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in s... more To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in smokers participating in a lung cancer screening trial. As part of the ethics-committee approved Dutch-Belgian Randomised Lung Cancer Multi-Slice Screening Trial (NELSON), computed tomography (CT) was used to screen 2994 current or former heavy smokers, aged 50-74 years, for lung cancer. CT was repeated after 1 and 3 years, with additional follow-up CT scans if necessary. All baseline CT scans were screened for nodules. Nodule volume was determined with automated volumetric analysis. Homogeneous solid nodules, attached to a fissure with a lentiform or triangular shape, were classified as PFNs. Nodules were considered benign if they did not grow during the total follow-up period or were proved to be benign in a follow-up by a pulmonologist. Prevalence, growth, and malignancy rate of PFNs were assessed. At baseline screening, 4026 nodules were detected in 1729 participants, and 19.7% (794 of 4026) of the nodules were classified as PFNs. The mean size of the PFNs was 4.4 mm (range: 2.8-10.6 mm) and the mean volume was 43 mm3 (range: 13-405 mm3). None of the PFNs were found to be malignant during follow-up. Between baseline and the first follow-up CT scan, 15.5% (123 of 794) were found to have grown, and 8.3% (66 of 794) had a volume doubling time of less than 400 days. One PFN was resected and proved to be a lymph node. PFNs are frequently found at CT scans for lung cancer. They can show growth rates in the range of malignant nodules, but none of the PFNs in the present study turned out to be malignant. Recognition of PFNs can reduce the number of follow-up examinations required for the workup of suspicious nodules.

Research paper thumbnail of The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study

Journal of thoracic imaging, Jan 18, 2016

The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the de... more The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the detection of common chest abnormalities. A total of 261 posteroanterior and lateral chest radiographs were collected from 2 hospitals. Radiographs could contain single or multiple focal opacities <3 cm (n=66), single or multiple focal opacities >3 cm (n=33), diffuse lung disease (n=49), signs of cardiogenic congestion (n=26), or no abnormalities (n=110). Twenty-one cases contained >1 type of disease. All abnormalities were confirmed by a computed tomographic scan obtained within 4 weeks of the radiograph. Bone-suppressed images (BSIs) were generated from every posteroanterior radiograph (ClearRead BSI 3.2). All cases were read by 6 radiologists without BSI, followed by an evaluation of the same case with BSI. Presence or absence of each disease category and confidence (0-100) of the observers were documented for each interpretation. Differences in the number of correct detections ...

Research paper thumbnail of Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Medical Physics, Jul 1, 2009

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung s... more Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Research paper thumbnail of Automated detection of nodules attached to the pleural and mediastinal surface in low-dose CT scans - art. no. 69150X

Proceedings of Spie the International Society For Optical Engineering, 2008

This paper presents a new computer-aided detection scheme for lung nodules attached to the pleura... more This paper presents a new computer-aided detection scheme for lung nodules attached to the pleural or mediastinal surface in low dose CT scans. First the lungs are automatically segmented and smoothed. Any connected set of voxels attached to the wall - with each voxel above minus 500 HU and the total object within a specified volume range - was considered a candidate finding. For each candidate, a refined segmentation was computed using morphological operators to remove attached structures. For each candidate, 35 features were defined, based on their position in the lung and relative to other structures, and the shape and density within and around each candidate. In a training procedure an optimal set of 15 features was determined with a k-nearest-neighbor classifier and sequential floating forward feature selection. The algorithm was trained with a data set of 708 scans from a lung cancer screening study containing 224 pleural nodules and tested on an independent test set of 226 scans from the same program with 58 pleural nodules. The algorithm achieved a sensitivity of 52% with an average of 0.76 false positives per scan. At 2.5 false positive marks per scan, the sensitivity increased to 80%.

Research paper thumbnail of Variability of Semiautomated Pulmonary Nodule Volume Measurements: A Comparison of 6 Lung Nodule Evaluation Software Packages

Research paper thumbnail of Workup of Suspicious Lesions on Digital Chest Radiography: Estimation of the Number of Unnecessary Follow-up CT Scans in Relation to the Threshold of Radiological Suspicion on Chest Radiographs

Research paper thumbnail of Automatic Estimation of Three-dimensional Lung Volume from Posterior-Anterior and Lateral Chest Radiographs

Research paper thumbnail of The Predictive Value of CT Quantified Pulmonary Emphysema on the Decline of Lung Function in Chronic Smokers: Results of a Long-term Follow-up Study

Research paper thumbnail of Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Medical Image Analysis, 2015

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodul... more In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

Research paper thumbnail of Performance Study of a Real-time Pulmonary Registration Algorithm for Chest Computed Tomography (CT)

PURPOSE A real-time registration algorithm for chest CT can reduce workload on radiologists evalu... more PURPOSE A real-time registration algorithm for chest CT can reduce workload on radiologists evaluating follow-up CTs, provided that it is fast and accurate. Objective of this study was to assess the accuracy of a single-point registration algorithm for chest CTs, using CT-pairs in which the patient was scanned twice in one session. METHOD AND MATERIALS Ten patients (8 men, 2 women, mean 58 yrs) referred for chest CT for known lung metastases, received two low-dose non-contrast-enhanced chest CTs (16 x 0.75mm collimation). Between these scans, patients got off and on the table to simulate a follow-up scan. Registration software was provided by Philips Research, Hamburg, Germany. Before scan evaluation, the software performs a global lung registration, individually for left and right lung. During evaluation only one user-selected point in space is registered resulting in a very short calculation time. Registration is based on a two step process. Step one compares the lung volume perce...

Research paper thumbnail of Computed Tomography Pulmonary Angiography in Acute Pulmonary Embolism

Journal of Thoracic Imaging, 2013

To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confiden... more To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. Using logistic regression for repeated measurements, we found a significant increase in readers&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; sensitivity (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001) without loss of specificity (P=0.855) with the effects being reader dependent (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). Sensitivities varied from 68% to 100% without CAD and from 76% to 100% with CAD. A 2-way analysis of variance showed a small but significant decrease in reading time (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001), with the duration varying between 24 and 208 seconds without CAD and between 17 and 196 seconds with CAD, and a significant increase in readers&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; confidence scores using CAD as a concurrent reader (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). CAD as a concurrent reader has the potential to increase…

Research paper thumbnail of <title>Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT</title>

Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 2008

Research paper thumbnail of <title>Reproducibility of airway wall thickness measurements</title>

Medical Imaging 2010: Computer-Aided Diagnosis, 2010

Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of c... more Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of chronic obstructive pulmonary disease (COPD), associated with reduced lung function in diseased individuals. Further investigation of this disease as well as monitoring of disease progression and treatment effect demand for accurate and reproducible assessment of airway wall thickness in CT datasets. With wall thicknesses

Research paper thumbnail of Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

The European respiratory journal, Jan 27, 2014

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A... more Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach. The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated. In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) pa...

Research paper thumbnail of Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy

Radiology, 2012

To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in s... more To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in smokers participating in a lung cancer screening trial. As part of the ethics-committee approved Dutch-Belgian Randomised Lung Cancer Multi-Slice Screening Trial (NELSON), computed tomography (CT) was used to screen 2994 current or former heavy smokers, aged 50-74 years, for lung cancer. CT was repeated after 1 and 3 years, with additional follow-up CT scans if necessary. All baseline CT scans were screened for nodules. Nodule volume was determined with automated volumetric analysis. Homogeneous solid nodules, attached to a fissure with a lentiform or triangular shape, were classified as PFNs. Nodules were considered benign if they did not grow during the total follow-up period or were proved to be benign in a follow-up by a pulmonologist. Prevalence, growth, and malignancy rate of PFNs were assessed. At baseline screening, 4026 nodules were detected in 1729 participants, and 19.7% (794 of 4026) of the nodules were classified as PFNs. The mean size of the PFNs was 4.4 mm (range: 2.8-10.6 mm) and the mean volume was 43 mm3 (range: 13-405 mm3). None of the PFNs were found to be malignant during follow-up. Between baseline and the first follow-up CT scan, 15.5% (123 of 794) were found to have grown, and 8.3% (66 of 794) had a volume doubling time of less than 400 days. One PFN was resected and proved to be a lymph node. PFNs are frequently found at CT scans for lung cancer. They can show growth rates in the range of malignant nodules, but none of the PFNs in the present study turned out to be malignant. Recognition of PFNs can reduce the number of follow-up examinations required for the workup of suspicious nodules.

Research paper thumbnail of Semi-Automatic Quantification of Subsolid Pulmonary Nodules: Comparison with Manual Measurements

Research paper thumbnail of Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Medical Physics, 2009

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung s... more Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Research paper thumbnail of Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels, and Bronchi

IEEE Transactions on Medical Imaging, 2000

Research paper thumbnail of Image subtraction facilitates assessment of volume and density change in ground-glass opacities in chest CT

Investigative radiology, 2009

To study the impact of image subtraction of registered images on the detection of change in pulmo... more To study the impact of image subtraction of registered images on the detection of change in pulmonary ground-glass nodules identified on chest CT.

Research paper thumbnail of Automatic segmentation of pulmonary segments from volumetric chest CT scans

IEEE transactions on medical imaging, 2009

Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of comp... more Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segmen...

Research paper thumbnail of Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy

Radiology, Aug 28, 2012

To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in s... more To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in smokers participating in a lung cancer screening trial. As part of the ethics-committee approved Dutch-Belgian Randomised Lung Cancer Multi-Slice Screening Trial (NELSON), computed tomography (CT) was used to screen 2994 current or former heavy smokers, aged 50-74 years, for lung cancer. CT was repeated after 1 and 3 years, with additional follow-up CT scans if necessary. All baseline CT scans were screened for nodules. Nodule volume was determined with automated volumetric analysis. Homogeneous solid nodules, attached to a fissure with a lentiform or triangular shape, were classified as PFNs. Nodules were considered benign if they did not grow during the total follow-up period or were proved to be benign in a follow-up by a pulmonologist. Prevalence, growth, and malignancy rate of PFNs were assessed. At baseline screening, 4026 nodules were detected in 1729 participants, and 19.7% (794 of 4026) of the nodules were classified as PFNs. The mean size of the PFNs was 4.4 mm (range: 2.8-10.6 mm) and the mean volume was 43 mm3 (range: 13-405 mm3). None of the PFNs were found to be malignant during follow-up. Between baseline and the first follow-up CT scan, 15.5% (123 of 794) were found to have grown, and 8.3% (66 of 794) had a volume doubling time of less than 400 days. One PFN was resected and proved to be a lymph node. PFNs are frequently found at CT scans for lung cancer. They can show growth rates in the range of malignant nodules, but none of the PFNs in the present study turned out to be malignant. Recognition of PFNs can reduce the number of follow-up examinations required for the workup of suspicious nodules.

Research paper thumbnail of The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study

Journal of thoracic imaging, Jan 18, 2016

The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the de... more The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the detection of common chest abnormalities. A total of 261 posteroanterior and lateral chest radiographs were collected from 2 hospitals. Radiographs could contain single or multiple focal opacities <3 cm (n=66), single or multiple focal opacities >3 cm (n=33), diffuse lung disease (n=49), signs of cardiogenic congestion (n=26), or no abnormalities (n=110). Twenty-one cases contained >1 type of disease. All abnormalities were confirmed by a computed tomographic scan obtained within 4 weeks of the radiograph. Bone-suppressed images (BSIs) were generated from every posteroanterior radiograph (ClearRead BSI 3.2). All cases were read by 6 radiologists without BSI, followed by an evaluation of the same case with BSI. Presence or absence of each disease category and confidence (0-100) of the observers were documented for each interpretation. Differences in the number of correct detections ...

Research paper thumbnail of Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Medical Physics, Jul 1, 2009

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung s... more Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Research paper thumbnail of Automated detection of nodules attached to the pleural and mediastinal surface in low-dose CT scans - art. no. 69150X

Proceedings of Spie the International Society For Optical Engineering, 2008

This paper presents a new computer-aided detection scheme for lung nodules attached to the pleura... more This paper presents a new computer-aided detection scheme for lung nodules attached to the pleural or mediastinal surface in low dose CT scans. First the lungs are automatically segmented and smoothed. Any connected set of voxels attached to the wall - with each voxel above minus 500 HU and the total object within a specified volume range - was considered a candidate finding. For each candidate, a refined segmentation was computed using morphological operators to remove attached structures. For each candidate, 35 features were defined, based on their position in the lung and relative to other structures, and the shape and density within and around each candidate. In a training procedure an optimal set of 15 features was determined with a k-nearest-neighbor classifier and sequential floating forward feature selection. The algorithm was trained with a data set of 708 scans from a lung cancer screening study containing 224 pleural nodules and tested on an independent test set of 226 scans from the same program with 58 pleural nodules. The algorithm achieved a sensitivity of 52% with an average of 0.76 false positives per scan. At 2.5 false positive marks per scan, the sensitivity increased to 80%.

Research paper thumbnail of Variability of Semiautomated Pulmonary Nodule Volume Measurements: A Comparison of 6 Lung Nodule Evaluation Software Packages

Research paper thumbnail of Workup of Suspicious Lesions on Digital Chest Radiography: Estimation of the Number of Unnecessary Follow-up CT Scans in Relation to the Threshold of Radiological Suspicion on Chest Radiographs

Research paper thumbnail of Automatic Estimation of Three-dimensional Lung Volume from Posterior-Anterior and Lateral Chest Radiographs

Research paper thumbnail of The Predictive Value of CT Quantified Pulmonary Emphysema on the Decline of Lung Function in Chronic Smokers: Results of a Long-term Follow-up Study

Research paper thumbnail of Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Medical Image Analysis, 2015

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodul... more In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

Research paper thumbnail of Performance Study of a Real-time Pulmonary Registration Algorithm for Chest Computed Tomography (CT)

PURPOSE A real-time registration algorithm for chest CT can reduce workload on radiologists evalu... more PURPOSE A real-time registration algorithm for chest CT can reduce workload on radiologists evaluating follow-up CTs, provided that it is fast and accurate. Objective of this study was to assess the accuracy of a single-point registration algorithm for chest CTs, using CT-pairs in which the patient was scanned twice in one session. METHOD AND MATERIALS Ten patients (8 men, 2 women, mean 58 yrs) referred for chest CT for known lung metastases, received two low-dose non-contrast-enhanced chest CTs (16 x 0.75mm collimation). Between these scans, patients got off and on the table to simulate a follow-up scan. Registration software was provided by Philips Research, Hamburg, Germany. Before scan evaluation, the software performs a global lung registration, individually for left and right lung. During evaluation only one user-selected point in space is registered resulting in a very short calculation time. Registration is based on a two step process. Step one compares the lung volume perce...

Research paper thumbnail of Computed Tomography Pulmonary Angiography in Acute Pulmonary Embolism

Journal of Thoracic Imaging, 2013

To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confiden... more To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. Using logistic regression for repeated measurements, we found a significant increase in readers&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; sensitivity (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001) without loss of specificity (P=0.855) with the effects being reader dependent (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). Sensitivities varied from 68% to 100% without CAD and from 76% to 100% with CAD. A 2-way analysis of variance showed a small but significant decrease in reading time (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001), with the duration varying between 24 and 208 seconds without CAD and between 17 and 196 seconds with CAD, and a significant increase in readers&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; confidence scores using CAD as a concurrent reader (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). CAD as a concurrent reader has the potential to increase…

Research paper thumbnail of <title>Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT</title>

Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 2008

Research paper thumbnail of <title>Reproducibility of airway wall thickness measurements</title>

Medical Imaging 2010: Computer-Aided Diagnosis, 2010

Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of c... more Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of chronic obstructive pulmonary disease (COPD), associated with reduced lung function in diseased individuals. Further investigation of this disease as well as monitoring of disease progression and treatment effect demand for accurate and reproducible assessment of airway wall thickness in CT datasets. With wall thicknesses

Research paper thumbnail of Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

The European respiratory journal, Jan 27, 2014

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A... more Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach. The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated. In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) pa...

Research paper thumbnail of Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy

Radiology, 2012

To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in s... more To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in smokers participating in a lung cancer screening trial. As part of the ethics-committee approved Dutch-Belgian Randomised Lung Cancer Multi-Slice Screening Trial (NELSON), computed tomography (CT) was used to screen 2994 current or former heavy smokers, aged 50-74 years, for lung cancer. CT was repeated after 1 and 3 years, with additional follow-up CT scans if necessary. All baseline CT scans were screened for nodules. Nodule volume was determined with automated volumetric analysis. Homogeneous solid nodules, attached to a fissure with a lentiform or triangular shape, were classified as PFNs. Nodules were considered benign if they did not grow during the total follow-up period or were proved to be benign in a follow-up by a pulmonologist. Prevalence, growth, and malignancy rate of PFNs were assessed. At baseline screening, 4026 nodules were detected in 1729 participants, and 19.7% (794 of 4026) of the nodules were classified as PFNs. The mean size of the PFNs was 4.4 mm (range: 2.8-10.6 mm) and the mean volume was 43 mm3 (range: 13-405 mm3). None of the PFNs were found to be malignant during follow-up. Between baseline and the first follow-up CT scan, 15.5% (123 of 794) were found to have grown, and 8.3% (66 of 794) had a volume doubling time of less than 400 days. One PFN was resected and proved to be a lymph node. PFNs are frequently found at CT scans for lung cancer. They can show growth rates in the range of malignant nodules, but none of the PFNs in the present study turned out to be malignant. Recognition of PFNs can reduce the number of follow-up examinations required for the workup of suspicious nodules.

Research paper thumbnail of Semi-Automatic Quantification of Subsolid Pulmonary Nodules: Comparison with Manual Measurements

Research paper thumbnail of Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Medical Physics, 2009

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung s... more Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Research paper thumbnail of Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels, and Bronchi

IEEE Transactions on Medical Imaging, 2000

Research paper thumbnail of Image subtraction facilitates assessment of volume and density change in ground-glass opacities in chest CT

Investigative radiology, 2009

To study the impact of image subtraction of registered images on the detection of change in pulmo... more To study the impact of image subtraction of registered images on the detection of change in pulmonary ground-glass nodules identified on chest CT.

Research paper thumbnail of Automatic segmentation of pulmonary segments from volumetric chest CT scans

IEEE transactions on medical imaging, 2009

Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of comp... more Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segmen...