Publications

The Effect of Radiation Dose Reduction on Computer-Aided Detection (CAD) Performance in a Low-Dose Lung Cancer Screening Population

Purpose Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-Aided Detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on…

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Automatic Anatomy Recognition in Medical Images

Automated analysis of medical images for diagnostic and data mining purposes are being widely developed. An essential step in high throughput processing is to know what anatomy is included in a given image so that the right analysis can be triggered automatically. CVIB PhD student Xiaoyong Wang, working with faculty and staff scientists, has developed an image classification technique using a feature vector based on image subsampling. Machine learning was performed using a large clinical trial database of labeled images.…

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Robustness-Driven Feature Selection in Classification of Interstitial Lung Disease

Lack of classifier robustness to CT acquisition parameter variations is a barrier to widespread adoption of CAD systems. As part of his PhD research, CVIB graduate Dr Danny Chong developed a new Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features that are relatively invariant CT technical factors. Working with CVIB faculty and staff, Dr. Chong showed that use of RDFS with 3D texture features improved CT classification of fibrotic interstitial lung disease in multi-center clinical trials. The research was…

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Toward clinically usable CAD for lung cancer screening with computed tomography

Objectives The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice. Methods A new automated lung nodule detection and measurement system was developed that incorporates…

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The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug…

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Automatic Segmentation of Lung Parenchyma in the Presence of Diseases Based on Curvature of Ribs

Rationale and Objectives Segmentation of lungs using high-resolution computer tomographic images in the setting of diffuse lung diseases is a major challenge in medical image analysis. Threshold-based techniques tend to leave out lung regions that have increased attenuation, such as in the presence of interstitial lung disease. In contrast, streak artifacts can cause the lung segmentation to “leak” into the chest wall. The purpose of this work was to perform segmentation of the lungs using a technique that selects an optimal threshold for a given patient…

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Knowledge‐based segmentation of thoracic computed tomography images for assessment of split lung function

The assessment of differential left and right lung function is important for patients under consideration for lung resection procedures such as single lung transplantation. We developed an automated, knowledge‐based segmentation algorithm for purposes of deriving functional information from dynamic computed tomography (CT) image data. Median lung attenuation (HU) and area measurements were automatically calculated for each lung from thoracic CT images acquired during a forced expiratory maneuver as indicators of the amount and rate of airflow. The accuracy of these…

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Patient-specific models for lung nodule detection and surveillance in CT images

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient’s baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic,…

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