An open-source toolkit for the volumetric measurement of CT lung lesions.

An open source lesion sizing toolkit has been developed with a general architecture for implementing lesion segmentation algorithms and a reference algorithm for segmenting solid and part-solid lesions from lung CT scans. The CT lung lesion segmentation algorithm detects four three-dimensional features corresponding to the lung wall, vasculature, lesion boundary edges, and low density background lung parenchyma. These features form boundaries and propagation zones that guide the evolution of a subsequent level set algorithm. User input is used to determine an initial seed point for the level set and users may also define a region of interest around the lesion. The methods are validated against 18 nodules using CT scans of an anthropomorphic thorax phantom simulating lung anatomy. The scans were acquired under differing scanner parameters to characterize algorithm behavior under varying acquisition protocols. We also validated repeatability using six clinical cases in which the patient was rescanned on the same day (zero volume change). The source code, data sets, and a running application are all provided under an unrestrictive license to encourage reproducibility and foster scientific exchange.

An analysis of early studies released by the Lung Imaging Database Consortium (LIDC).

RATIONALE AND OBJECTIVES: To analyze radiologist lung nodule segmentations in the Lung Imaging Database Consortium (LIDC) database and to apply statistical tools to generate estimates of ground truth. This investigation expands on earlier work by considering a larger number of cases from the LIDC database, and results were generated on a per-nodule basis, as opposed to a per-case basis as was done previously. MATERIALS AND METHODS: We analyzed nodule data drawn from the 41 most recent computed tomography exams released by the LIDC. We combined radiologist segmentations for a given nodule using different consensus schemes: union, intersection, and simultaneous truth and performance level estimation (STAPLE). We also generated three-dimensional models of the manual segmentations using discrete marching cubes to visualize features of the data. RESULTS: Using the union as the consensus scheme produced the greatest number of nodule-positive voxels while using the intersection produced the fewest. Considering only nodules for which all readers agreed on nodule presence, STAPLE computed sensitivity averages for readers one, two, three, and four were 0.91, 0.83, 0.90, and 0.77, respectively. Specificity averages were 0.97, 0.98, 0.97, and 0.97. Considering cases for which there was disagreement about nodule presence, sensitivity results become 0.67, 0.74, 0.60, and 0.37. Specificity results in this case are 0.95, 0.95, 0.95, and 0.98. STAPLE-generated pmaps exhibited probability values tightly grouped below the 0.25 and above the 0.75 probability levels. Three-dimensional models of manually segmented nodules revealed step-artefacts in the segmentation data. CONCLUSIONS: Radiologists often disagree about nodule presence. Ideally, knowing each reader's sensitivity and specificity a priori is preferred for optimal STAPLE results. Knowing these values and developing manual segmentation tools and imaging protocols that mitigate unwanted segmentation features (such as step artefacts) can result in more accurate estimates of ground truth. Furthermore, a computer-aided detection algorithm's performance is a function of the ground truth estimate by which it is scored.

Part-based local shape models for colon polyp detection.

This paper presents a model-based technique for lesion detection in colon CT scans that uses analytical shape models to map the local shape curvature at individual voxels to anatomical labels. Local intensity profiles and curvature information have been previously used for discriminating between simple geometric shapes such as spherical and cylindrical structures. This paper introduces novel analytical shape models for colon-specific anatomy, viz. folds and polyps, built by combining parts with simpler geometric shapes. The models better approximate the actual shapes of relevant anatomical structures while allowing the application of model-based analysis on the simpler model parts. All parameters are derived from the analytical models, resulting in a simple voxel labeling scheme for classifying individual voxels in a CT volume. The algorithm's performance is evaluated against expert-determined ground truth on a database of 42 scans and performance is quantified by free-response receiver-operator curves.

An analysis of early studies released by the Lung Imaging Database Consortium (LIDC).

Lung cancer remains an ongoing problem resulting in substantial deaths in the United States and the world. Within the United states, cancer of the lung and bronchus are the leading causes of fatal malignancy and make up 32% of the cancer deaths among men and 25% of the cancer deaths among women. Five year survival is low, (14%), but recent studies are beginning to provide some hope that we can increase survivability of lung cancer provided that the cancer is caught and treated in early stages. These results motivate revisiting the concept of lung cancer screening using thin slice multidetector computed tomography (MDCT) protocols and automated detection algorithms to facilitate early detection. In this environment, resources to aid Computer Aided Detection (CAD) researchers to rapidly develop and harden detection and diagnostic algorithms may have a significant impact on world health. The National Cancer Institute (NCI) formed the Lung Imaging Database Consortium (LIDC) to establish a resource for detecting, sizing, and characterizing lung nodules. This resource consists of multiple CT chest exams containing lung nodules that seveal radiologists manually countoured and characterized. Consensus on the location of the nodule boundaries, or even on the existence of a nodule at a particular location in the lung was not enforced, and each contour is considered a possible nodule. The researcher is encouraged to develop measures of ground truth to reconcile the multiple radiologist marks. This paper analyzes these marks to determine radiologist agreement and to apply statistical tools to the generation of a nodule ground truth. Features of the resulting consensus and individual markings are analyzed.

Model-based analysis of local shape for lesion detection in CT scans.

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.