Keelin Murphy

elastix : A Toolbox for Intensity-Based Medical Image Registration

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

Pulmonary Ground-Glass Nodules: Increase in Mass as an Early Indicator of Growth

PURPOSE To compare manual measurements of diameter, volume, and mass of pulmonary ground-glass nodules (GGNs) to establish which method is best for identifying malignant GGNs by determining change across time. MATERIALS AND METHODS In this ethics committee-approved retrospective study, baseline and follow-up CT examinations of 52 GGNs detected in a lung cancer screening trial were included, resulting in 127 GGN data sets for evaluation. Two observers measured GGN diameter with electronic calipers, manually outlined GGNs to obtain volume and mass, and scored whether a solid component was present. Observer 1 repeated all measurements after 2 months. Coefficients of variation and limits of agreement were calculated by using Bland-Altman methods. In a subgroup of GGNs containing all resected malignant lesions, the ratio between intraobserver variability and growth (growth-to-variability ratio) was calculated for each measurement technique. In this subgroup, the mean time for growth to exceed the upper limit of agreement of each measurement technique was determined. RESULTS The kappa values for intra- and interobserver agreement for identifying a solid component were 0.55 and 0.38, respectively. Intra- and interobserver coefficients of variation were smallest for GGN mass (P < .001). Thirteen malignant GGNs were resected. Mean growth-to-variability ratios were 11, 28, and 35 for diameter, volume, and mass, respectively (P = .03); mean times required for growth to exceed the upper limit of agreement were 715, 673, and 425 days, respectively (P = .02). CONCLUSION Mass measurements can enable detection of growth of GGNs earlier and are subject to less variability than are volume or diameter measurements.

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

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.

Semi-automatic construction of reference standards for evaluation of image registration

Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic fashion. A well-distributed set of n landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers define corresponding anatomic locations in the second scan for a specified subset of s of these landmarks. The remaining n-s landmarks are matched fully automatically by a thin-plate-spline based system using the s manual landmark correspondences to model the relationship between the scans. The method is applied to 47 pairs of temporal thoracic CT scans, three pairs of brain MR scans and five thoracic CT datasets with synthetic deformations. Interobserver differences are used to demonstrate the accuracy of the matched points. The utility of the reference standard data as a tool in evaluating registration is shown by the comparison of six sets of registration results on the 47 pairs of thoracic CT data.

Evaluation of 4D-CT Lung Registration

Non-rigid registration accuracy assessment is typically performed by evaluating the target registration error at manually placed landmarks. For 4D-CT lung data, we compare two sets of landmark distributions: a smaller set primarily defined on vessel bifurcations as commonly described in the literature and a larger set being well-distributed throughout the lung volume. For six different registration schemes (three in-house schemes and three schemes frequently used by the community) the landmark error is evaluated and found to depend significantly on the distribution of the landmarks. In particular, lung regions near to the pleura show a target registration error three times larger than near-mediastinal regions. While the inter-method variability on the landmark positions is rather small, the methods show discriminating differences with respect to consistency and local volume change. In conclusion, both a well-distributed set of landmarks and a deformation vector field analysis are necessary for reliable non-rigid registration accuracy assessment.

The relationship between lung function impairment and quantitative computed tomography in chronic obstructive pulmonary disease.

AbstractObjectivesTo determine the relationship between lung function impairment and quantitative computed tomography (CT) measurements of air trapping and emphysema in a population of current and former heavy smokers with and without airflow limitation.MethodsIn 248 subjects (50 normal smokers; 50 mild obstruction; 50 moderate obstruction; 50 severe obstruction; 48 very severe obstruction) CT emphysema and CT air trapping were quantified on paired inspiratory and end-expiratory CT examinations using several available quantification methods. CT measurements were related to lung function (FEV1, FEV1/FVC, RV/TLC, Kco) by univariate and multivariate linear regression analysis.ResultsQuantitative CT measurements of emphysema and air trapping were strongly correlated to airflow limitation (univariate r-squared up to 0.72, p < 0.001). In multivariate analysis, the combination of CT emphysema and CT air trapping explained 68-83% of the variability in airflow limitation in subjects covering the total range of airflow limitation (p < 0.001).ConclusionsThe combination of quantitative CT air trapping and emphysema measurements is strongly associated with lung function impairment in current and former heavy smokers with a wide range of airflow limitation.Key Points• CT helps to automatically assess lung disease in heavy smokers • CT quantitatively measures emphysema and small airways disease in heavy smokers • CT air trapping and CT emphysema are associated with lung function impairment

Semi-automatic Reference Standard Construction for Quantitative Evaluation of Lung CT Registration

An algorithm is presented for the efficient semi-automatic construction of a detailed reference standard for registration in thoracic CT. A well-distributed set of 100 landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers locate corresponding anatomic locations in the second scan. The manual annotations are used to learn the relationship between the scans and after approximately twenty manual marks the remaining points are matched automatically. Inter-observer differences demonstrate the accuracy of the matching and the applicability of the reference standard is demonstrated on two different sets of registration results over 19 CT scan pairs.

Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT

PURPOSE To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry. METHODS A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage. RESULTS The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one. CONCLUSIONS Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.

Supervised quality assessment of medical image registration: application to intra-patient CT lung registration.

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different non-rigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%.