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 Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" noduleâ�¥3 mm," " nodule<3 mm," and "non- noduleâ�¥3 mm "). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked " nodulâ�¥3 mm " by at least one radiologist, of which 928 (34.7) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice. Â© 2011 U.S. Government.

/pdf/the-lung-image-database-consortium-lidc-and-image-database-2liy070dpe.pdf

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines1–6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7–10. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide. A convolutional neural network performs automated prediction of malignancy risk of pulmonary nodules in chest CT scan volumes and improves accuracy of lung cancer screening.

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.

http://www.fjt.info.gifu-u.ac.jp/publication/661.pdf

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

The LIDC is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource. A unique multi-center data collection process and communication system were developed to share image data and to capture the location and spatial extent of lung nodules as marked by expert radiologists. A two-phase data collection process was designed to allow multiple radiologists at different centers to asynchronously review and annotate each CT image series. Four radiologists reviewed each case using this process. In the first or "blinded" phase, each radiologist reviewed the CT series independently. In the second or "unblinded" review phase, the results from all four blinded reviews are compiled and presented to each radiologist for a second review. This allows each radiologist to review their own annotations along with those of the other radiologists. The results from each radiologist's unblinded review were compiled to form the final unblinded review. There is no forced consensus in this process. An XML-based message system was developed to communicate the results of each reading. This two-phase data collection process was designed, tested and implemented across the LIDC. It has been used for more than 130 CT cases that have been read and annotated by four expert readers and are publicly available at (http://ncia.nci.nih.gov). A data collection process was developed, tested and implemented that allowed multiple readers to review each case multiple times and that allowed each reader to observe the annotations of other readers.

/pdf/the-lung-image-database-consortium-lidc-data-collection-3flf3tmots.pdf

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

Randomly selected pathology sections of lung tissue are used to correlate lung pathology with Computer Tomography (CT) images. The randomly selected pathology sections provide physicians with little freedom to thoroughly investigate specific areas of interest as identified via CT images. A Large Image Microscope Array (LIMA) was designed to serially section and image entire organs for direct correlation between lung pathology and CT. The LIMA consists of a novel vibratome, capable of sectioning tissue down to a thickness of 40mm at specimen dimensions of 20cm by 30cm to a total depth of 30cm. A camera and a stereomicroscope, mounted on a XYZ gantry above the vibratome is moved through an automated raster scan to capture the entire surface area of the tissue via many high magnification images. A custom software program was developed to automate all hardware components. The alignment and stitching of the images is achieved though custom C++ code in conjunction with the Insight Segmentation and Registration Toolkit (ITK). The resulting high magnification, high-resolution pathology images are registered with corresponding CT images. Through point-to-point correlation between the two imaging techniques a pathological and CT ground truth may be established.

A whole organ serial sectioning and imaging system for correlation of pathology to computer tomography

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The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

A whole organ serial sectioning and imaging system for correlation of pathology to computer tomography