On combining computer-aided detection systems

Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis

Tuberculosis (TB) is a common disease with high mortality and morbidity rates worldwide. Automatic systems to detect TB on chest radiographs (CXRs) can improve the efficiency of diagnostic algorithms for pulmonary TB. The diverse manifestation of TB on CXRs from different populations requires a system that can be adapted to deal with different types of abnormalities. A computer aided detection (CAD) system was developed which combines several subscores of supervised subsystems detecting textural, shape, and focal abnormalities into one TB score. A general framework was developed to combine an arbitrary number of subscores: subscores were normalized, collected in a feature vector and then combined using a supervised classifier into one combined score. The method was evaluated on two databases, both consisting of 200 digital CXRs, from: (A) Western high-risk group screening, (B) TB suspect screening in Africa. The subscores and combined score were compared to (1) an external, non-radiological, reference and (2) a radiological reference determined by a human expert. Performance was measured using Receiver Operator Characteristic (ROC) analysis. Different subscores performed best in the two databases. The combined TB score performed better than the individual subscores, except for the external reference in database B. The performances of the independent observer were slightly higher than the combined TB score. Compared to the external reference, differences in performance between the combined TB score and the independent observer were not significant in both databases. Supervised combination to compute an overall TB score allows for a necessary adaptation of the CAD system to different settings or different operational requirements.

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

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.

Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms

PURPOSE

To develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) which can make use of an existing CAD system for detection of breast masses in full-field digital mammography (FFDM). This approach has the advantage that large digital screening databases that are becoming available can be used for training. DBT is currently not used for screening which makes it hard to obtain sufficient data for training.

METHODS

The proposed CAD system is applied to reconstructed DBT volumes and consists of two stages. In the first stage, an existing 2D CAD system is applied to slabs composed of multiple DBT slices, after processing the slabs to a representation similar to that of the FFDM training data. In the second stage, the authors group detections obtained in the slabs that detect the same object and determine the 3D location of the grouped findings using one of three different approaches, including one that uses a set of features extracted from the DBT slabs. Experiments were conducted to determine performance of the CAD system, the optimal slab thickness for this approach and the best method to establish the 3D location. Experiments were performed using a database of 192 patients (752 DBT volumes). In 49 patients, one or more malignancies were present which were described as a mass, architectural distortion, or asymmetry. Free response receiver operating characteristic analysis and bootstrapping were used for statistical evaluation.

RESULTS

Best performance was obtained when slab thickness was in the range of 1-2 cm. Using the feature based 3D localization procedure developed in the study, accurate 3D localization could be obtained in most cases. Case sensitivities of 80% and 90% were achieved at 0.35 and 0.99 false positives per volume, respectively.

CONCLUSIONS

This study indicates that there may be a large benefit in using 2D mammograms for the development of CAD for DBT and that there is no need to exclusively limit development to DBT data.

Area--a consistent method to evaluate pulmonary tumor size on multidetector CT imaging: an intraobserver and interobserver agreement study

OBJECTIVES

To evaluate the agreements of unidimensional, bidimensional, area and volume measurements of pulmonary tumors on multidetector computed tomography (MDCT), and to determine which method is the most reliable one.

MATERIALS AND METHODS

Thirty patients with pulmonary tumors were enrolled in this study, which referred to undergo thoracic MDCT in our hospital. Four radiologists evaluated dimensions of pulmonary tumor independently, including length, width, height, area and volume. The intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) were both used to evaluate the variability between the repeat readings of the same scan.

RESULTS

The ICCs and CCCs of the intraobserver were both higher than interobserver's (ICC intra vs. inter: 0.984 vs. 0.947 and CCC intra vs. inter: 0.993 vs. 0.943). Area of intraobserver ICC (ICC=0.992, P<.001) and CCC (CCC=0.997, P<.001) both had the best agreements of the six methods. Among the interobserver ICCs and CCCs, area (ICC=0.981, P<.001 and CCC=0.982, P<.001) was also the best of the six methods.

CONCLUSIONS

Area measurement on MDCT is the most reproducible method that measures tumor dimension accurately.

Advances and perspectives in lung cancer imaging using multidetector row computed tomography

The introduction of multidetector row computed tomography (CT) into clinical practice has revolutionized many aspects of the clinical work-up. Lung cancer imaging has benefited from various breakthroughs in computing technology, with advances in the field of lung cancer detection, tissue characterization, lung cancer staging and response to therapy. Our paper discusses the problems of radiation, image visualization and CT examination comparison. It also reviews the most significant advances in lung cancer imaging and highlights the emerging clinical applications that use state of the art CT technology in the field of lung cancer diagnosis and follow-up.

Validation of tablet-based evaluation of color fundus images

PURPOSE

To compare diabetic retinopathy (DR) referral recommendations made by viewing fundus images using a tablet computer with those made using a standard desktop display.

METHODS

A tablet computer (iPad) and a desktop computer with a high-definition color display were compared. For each platform, 2 retinal specialists independently rated 1,200 color fundus images from patients at risk for DR using an annotation program Truthseeker. The specialists determined whether each image had referable DR and also how urgently each patient should be referred for medical examination. Graders viewed and rated the randomly presented images independently and were masked to their ratings on the alternative platform. Tablet-based and desktop display-based referral ratings were compared using cross-platform intraobserver kappa as the primary outcome measure. Additionally, interobserver kappa, sensitivity, specificity, and area under the receiver operating characteristic were determined.

RESULTS

A high level of cross-platform intraobserver agreement was found for the DR referral ratings between the platforms (κ = 0.778) and for the 2 graders (κ = 0.812). Interobserver agreement was similar for the 2 platforms (κ = 0.544 and κ = 0.625 for tablet and desktop, respectively). The tablet-based ratings achieved a sensitivity of 0.848, a specificity of 0.987, and an area under the receiver operating characteristic of 0.950 compared with desktop display-based ratings.

CONCLUSION

In this pilot study, tablet-based rating of color fundus images for subjects at risk for DR was consistent with desktop display-based rating. These results indicate that tablet computers can be reliably used for clinical evaluation of fundus images for DR.

Extraction of airways from CT (EXACT'09)

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

Evaluation of the grading performance of an ensemble-based microaneurysm detector

In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our research group was used; this provided promising results in our earlier experiments. At its best, the 1200 image of the Messidor database is classified by this detector with a sensitivity of 96%, a specificity of 51% and achieved an AUC of 0.87. As anticipated, larger microaneurysm counts are recognized with higher level of certainty. Therefore, this approach might be expected to have good performance in relation to the severity of the disease.