Aria Pezeshk

3D convolutional neural network for automatic detection of lung nodules in chest CT

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

Seamless insertion of real pulmonary nodules in chest CT exams

The availability of large medical image datasets is critical in many applications such as training and testing of computer aided diagnosis (CAD) systems, evaluation of segmentation algorithms, and conducting perceptual studies. However, collection of large repositories of clinical images is hindered by the high cost and difficulties associated with both the accumulation of data and establishment of the ground truth. To address this problem, we are developing an image blending tool that allows users to modify or supplement existing datasets by seamlessly inserting a real lesion extracted from a source image into a different location on a target image. In this study we focus on the application of this tool to pulmonary nodules in chest CT exams. We minimize the impact of user skill on the perceived quality of the blended image by limiting user involvement to two simple steps: the user first draws a casual boundary around the nodule of interest in the source, and then selects the center of desired insertion area in the target. We demonstrate examples of the performance of the proposed system on samples taken from the Lung Image Database Consortium (LIDC) dataset, and compare the noise power spectrum (NPS) of blended nodules versus that of native nodules in simulated phantoms.

Seamless lesion insertion in digital mammography: methodology and reader study

Collection of large repositories of clinical images containing verified cancer locations is costly and time consuming due to difficulties associated with both the accumulation of data and establishment of the ground truth. This problem poses a significant challenge to the development of machine learning algorithms that require large amounts of data to properly train and avoid overfitting. In this paper we expand the methods in our previous publications by making several modifications that significantly increase the speed of our insertion algorithms, thereby allowing them to be used for inserting lesions that are much larger in size. These algorithms have been incorporated into an image composition tool that we have made publicly available. This tool allows users to modify or supplement existing datasets by seamlessly inserting a real breast mass or micro-calcification cluster extracted from a source digital mammogram into a different location on another mammogram. We demonstrate examples of the performance of this tool on clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM). Finally, we report the results of a reader study evaluating the realism of inserted lesions compared to clinical lesions. Analysis of the radiologist scores in the study using receiver operating characteristic (ROC) methodology indicates that inserted lesions cannot be reliably distinguished from clinical lesions.

Improving CAD performance by seamless insertion of pulmonary nodules in chest CT exams

The availability of large medical image datasets is critical in training and testing of computer aided diagnosis (CAD) systems. However, collection of data and establishment of ground truth for medical images are both costly and difficult. To address this problem, we have developed an image composition tool that allows users to modify or supplement existing datasets by seamlessly inserting a clinical lesion extracted from a source image into a different location on a target image. In this study we focus on the application of this tool to the training of a CAD system designed to detect pulmonary nodules in chest CT. To compare the performance of a CAD system without and with the use of our image composition tool, we trained the system on two sets of data. The first training set was obtained from original CT cases, while the second set consisted of the first set plus nodules in the first set inserted into new locations. We then compared the performance of the two CAD systems in differentiating nodules from normal areas by testing each trained system against a fixed dataset containing natural nodules, and using the area under the ROC curve (AUC) as the figure of merit. The performance of the system trained with the augmented dataset was found to be significantly better than that trained with the original dataset under several training scenarios.

Deep learning in medical imaging and radiation therapy

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

Comparison of two classifiers when the data sets are imbalanced: the power of the area under the precision-recall curve as the figure of merit versus the area under the ROC curve

In many two-class problems in automated classification and information retrieval, the classes are imbalanced, and the separation between the positive and negative classes is large. The precision-recall (PR) curve has been suggested as an alternative to the receiver operating characteristic (ROC) curve to characterize the performance of automated systems when the classes are imbalanced, and the area under the precision-recall curve (AUCPR) has been suggested as an alternative performance measure to the area under the ROC curve (AUCROC). AUCPR and AUCROC are distinct measures of performance, even though the relationship between the precision-recall and ROC curves is well-known. In this study, we compared the statistical power of the AUCPR to that of the AUCROC. Our results indicate that the AUCPR can offer a small statistical advantage when the prevalence is low and the separation between the positive and negative classes is large. When the data set is more balanced or the separation between the classes is low or moderate, AUCROC has slightly higher power.

Semi-parametric estimation of the area under the precision-recall curve

Precision and recall are two common metrics used in the evaluation of information retrieval systems. By changing the number of retrieved documents, one can obtain a precision-recall curve. The area under the precision-recall curve (AUCPR) has been suggested as a performance measure for information retrieval systems, in a manner similar to the use of the area under the receiver operating characteristic curve in binary classification. Limited work has been performed in the literature to investigate the bias and variance of AUCPR estimators. The goal of our study was to investigate the bias and variability of a semi-parametric binormal method for estimating the AUCPR, and to compare it to other techniques, such as average precision (AP) and lower trapezoid (LT) approximation. We show how AUCPR can be obtained given the binormal model parameters, and how its variance can be estimated using the delta method. We performed simulation experiments with normal and non-normal data, and investigated the effect of sample size and prevalence. Our results indicated that the semi-parametric binormal approach provided AUCPR estimates with small bias and confidence intervals with acceptable coverage when the sample size was large, and the performance of the binormal model was comparable to or better than alternative methods evaluated in this study when the sample size was small. We conclude that the semi-parametric binormal model can be used to accurately estimate the AUCPR, and that the confidence intervals derived from the model can be at least as accurate as from other alternatives, even for non-normal decision variable distributions.

CT image quality evaluation for detection of signals with unknown location, size, contrast and shape using unsupervised methods

The advent of new image reconstruction and image processing techniques for CT images has increased the need for robust objective image quality assessment methods. One of the most common quality assessment methods is the measurement of signal detectability for a known signal at a known location using supervised classification techniques. However, this method requires a large number of simulations or physical measurements, and its underlying assumptions may be considered clinically unrealistic. In this study we focus on objective assessment of image quality in terms of detection of a signal with unknown location, size, shape, and contrast. We explore several unsupervised saliency detection methods which assume no knowledge about the signal, along with a template matching technique which uses information about the signals size and shape in the object domain, for simulated phantoms that have been reconstructed using filtered back projection (FBP) and iterative reconstruction algorithms (IRA). The performance of each of the image reconstruction algorithms is then measured using the area under the localization receiver operating characteristic curve (LROC) and exponential transformation of the free response operating characteristic curve (EFROC). Our results indicate that unsupervised saliency detection methods can be effectively used to determine image quality in terms of signal detectability for unknown signals given only a small number of sample images.

Comparison of two stand-alone CADe systems at multiple operating points

Computer-aided detection (CADe) systems are typically designed to work at a given operating point: The device displays a mark if and only if the level of suspiciousness of a region of interest is above a fixed threshold. To compare the standalone performances of two systems, one approach is to select the parameters of the systems to yield a target false-positive rate that defines the operating point, and to compare the sensitivities at that operating point. Increasingly, CADe developers offer multiple operating points, which necessitates the comparison of two CADe systems involving multiple comparisons. To control the Type I error, multiple-comparison correction is needed for keeping the family-wise error rate (FWER) less than a given alpha-level. The sensitivities of a single modality at different operating points are correlated. In addition, the sensitivities of the two modalities at the same or different operating points are also likely to be correlated. It has been shown in the literature that when test statistics are correlated, well-known methods for controlling the FWER are conservative. In this study, we compared the FWER and power of three methods, namely the Bonferroni, step-up, and adjusted step-up methods in comparing the sensitivities of two CADe systems at multiple operating points, where the adjusted step-up method uses the estimated correlations. Our results indicate that the adjusted step-up method has a substantial advantage over other the two methods both in terms of the FWER and power.

Investigation of methods for calibration of classifier scores to probability of disease

Classifier scores in many diagnostic devices, such as computer-aided diagnosis systems, are usually on an arbitrary scale, the meaning of which is unclear. Calibration of classifier scores to a meaningful scale such as the probability of disease is potentially useful when such scores are used by a physician or another algorithm. In this work, we investigated the properties of two methods for calibrating classifier scores to probability of disease. The first is a semiparametric method in which the likelihood ratio for each score is estimated based on a semiparametric proper receiver operating characteristic model, and then an estimate of the probability of disease is obtained using the Bayes theorem assuming a known prevalence of disease. The second method is nonparametric in which isotonic regression via the pool-adjacent-violators algorithm is used. We employed the mean square error (MSE) and the Brier score to evaluate the two methods. We evaluate the methods under two paradigms: (a) the dataset used to construct the score-to-probability mapping function is used to calculate the performance metric (MSE or Brier score) (resubstitution); (b) an independent test dataset is used to calculate the performance metric (independent). Under our simulation conditions, the semiparametric method is found to be superior to the nonparametric method at small to medium sample sizes and the two methods appear to converge at large sample sizes. Our simulation results also indicate that the resubstitution bias may depend on the performance metric and, for the semiparametric method, the resubstitution bias is small when a reasonable number of cases (> 100 cases per class) are available.