Sophie Paquerault

Improvement of computerized mass detection on mammograms: Fusion of two‐view information

Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more useful if the false-positive rate is reduced. Current CAD systems generally detect and characterize suspicious abnormal structures in individual mammographic images. Clinical experiences by radiologists indicate that screening with two mammographic views improves the detection accuracy of abnormalities in the breast. It is expected that the fusion of information from different mammographic views will improve the performance of CAD systems. We are developing a two-view matching method that utilizes the geometric locations, and morphological and textural features to correlate objects detected in two different views using a prescreening program. First, a geometrical model is used to predict the search region for an object in a second view from its location in the first view. The distance between the object and the nipple is used to define the search area. After pairing the objects in two views, textural and morphological characteristics of the paired objects are merged and similarity measures are defined. Linear discriminant analysis is then employed to classify each object pair as a true or false mass pair. The resulting object correspondence score is combined with its one-view detection score using a fusion scheme. The fusion information was found to improve the lesion detectability and reduce the number of FPs. In a preliminary study, we used a data set of 169 pairs of cranio-caudal (CC) and mediolateral oblique (MLO) view mammograms. For the detection of malignant masses on current mammograms, the film-based detection sensitivity was found to improve from 62% with a one-view detection scheme to 73% with the new two-view scheme, at a false-positive rate of 1 FP/image. The corresponding cased-based detection sensitivity improved from 77% to 91%.

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images.

Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.

ADVANTAGES AND EXAMPLES OF RESAMPLING FOR CAD EVALUATION

Comparison of performance accuracy between different computer-aided diagnosis (CAD) devices is a challenging task. Anatomical structure of the patient and imaging geometry introduce many possible correlations among scores produced by a CAD. Numerous analysis methods have been designed to account for the correlations among CAD scores or the variable number of CAD marks, but usually not both. However, methods that resample by case incorporate both of these sources of variability while accounting for in-case correlations. In this paper we present some examples of the use of resampling on CAD score data

Improvement of mammographic lesion detection by fusion of information from different views

In screening mammography, two standard views, craniocaudal (CC) and medio-lateral oblique (MLO), are commonly taken, and radiologists use information from the two views for lesion detection and diagnosis. Current computer-aided diagnosis (CAD) systems are designed to detect lesions on each view separately. We are developing a CAD method that utilizes information from the two views to reduce false-positives (FPs). Our two-view detection scheme consists of two main stages, a one-view pre-screening stage and a two-view correspondence stage. The one-view and two-view scores are then fused to estimate the likelihood that an object is a true mass. In this study, we analyzed the effectiveness of the proposed fusion scheme for FP reduction and its dependence on the number of objects per image in the pre-screening stage. The preliminary results demonstrate that the fusion of information from the CC and MLO views significantly reduced the FP rate in comparison to the one-view scheme. When the pre-screening stage produced 10 objects per image, the two-view fusion technique reduced the FP rate from an average of 2.1 FPs/image in our current one-view CAD scheme to 1.2 FPs/image at a sensitivity of 80%. The results also indicate that the improvement in the detection accuracy was essentially independent of the number of initial objects per image obtained at the pre-screening stage for this data set.

Investigation of Reading Mode and Relative Sensitivity as Factors That Influence Reader Performance When Using Computer-Aided Detection Software

RATIONALE AND OBJECTIVES The aim of this study was to investigate the effects of relative sensitivity (reader without computer-aided detection [CAD] vs stand-alone CAD) and reading mode on reader performance when using CAD software. MATERIALS AND METHODS Two sets of 100 images (low-contrast and high-contrast sets) were created by adding low-contrast or high-contrast simulated masses to random locations in 100 normal mammograms. This produced a relative sensitivity, substantially less for the low-contrast set and similar for the high-contrast set. Seven readers reviewed every image in each set and specified location and probability scores using three reading modes (without CAD, second read with CAD, and concurrent read with CAD). Reader detection accuracy was analyzed using areas under free-response receiver operating characteristic curves, sensitivity, and the number of false-positive findings per image. RESULTS For the low-contrast set, average differences in areas under free-response receiver operating characteristic curves, sensitivity, and false-positive findings per image without CAD were 0.02, 0.12, and 0.11, respectively, compared to second read and 0.05, 0.17, and 0.09 (not statistically significant), respectively, compared to concurrent read. For the high-contrast set, average differences were 0.002 (not statistically significant), 0.04, and 0.05, respectively, compared to second read and -0.004 (not statistically significant), 0.04, and 0.08 (not statistically significant), respectively, compared to concurrent read (all differences were statistically significant except as noted). Differences were greater in the low-contrast set than the high-contrast set. Differences between second read and concurrent read were not significant. CONCLUSIONS Relative sensitivity is a critical factor that determines incremental improvement in reader performance when using CAD and appears to be more important than reading mode. Relative sensitivity may determine the clinical usefulness of CAD in different clinical applications and for different types of users.

Observer evaluation of computer-aided detection: second reader versus concurrent reader scenario

We are comparing the performance of computer-aided detection (CAD) used as a second reader to concurrent-use CAD. We have designed a multi-reader multi-case (MRMC) observer study using fixed-size mammographic background images with fixed intensity Gaussian signals added in two experiments. A CAD system was developed to automatically detect these signals. The two experiments utilized signals of different contrast levels to assess the impact of CAD when the standalone CAD sensitivity was superior (low contrast) or equivalent (high contrast) to the average reader in the study. Seven readers participated in the study and were asked to review 100 images, identify signal locations, and rate each on a 100-point scale. A rating of 50 was used as a cutpoint and provided a binary classification of each candidate. Readers read the case set using CAD in both the second-reader and concurrent-reader scenarios. Results from the different signal intensities and reading paradigms were analyzed using the area under the Free-response Receiver Operating Characteristics curves. Sensitivity and the average number of FPs/image were also determined. The results showed that CAD, either used as a second reader or as a concurrent reader, can increase reader sensitivity but with an increase in FPs. The study demonstrated that readers may benefit from concurrent CAD when CAD standalone performance outperforms average reader sensitivity. However, this trend was not observed when CAD performance was equivalent to the sensitivity of the average reader.

Interval change analysis in temporal pairs of mammograms using a local affine transformation

The aim of this study is to evaluate the use of a local affine transformation for computer-aided interval change analysis in mammography. A multistage regional registration technique was developed for identifying masses on temporal pairs of mammograms. In the first stage, the breast images from the current and prior mammograms were globally aligned. An initial fan-shape search region was defined on the prior mammogram. In the second stage, the location of the fan-shape region was refined by warping, based on an affine transformation and simplex optimization. A new refined search region was defined on the prior mammogram. In the third stage a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of mammograms containing biopsy-proven masses. Eighty-six percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations. The average distance between the estimated and the true centroid of the lesions on the prior mammogram was 4.4 +/- 5.9 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of mammograms. This improvement gain is mainly from the local affine transformation.

Breast imaging going virtual: is that a reality?

Current research and developments in the technology of imaging have led to explosive growth in the interdisciplinary field of imaging science. This is particularly evident in medical imaging. There is unfortunately no perfect medical imaging technology that can reveal all aspects of disease on the same image, and different medical imaging technologies often reveal different characteristics of disease. For example, x-ray mammography can reveal malignant microcalcifications, but cannot reveal information on tumor vascularity. On the other hand, magnetic resonance imaging (MRI) cannot detect microcalcifications, but relies primarily on differences in vascularity—as depicted by differences in degree and timing of contrast uptake—to detect tumors. However, MRI contrast enhancement in premenopausal patients varies greatly with the menstrual cycle, and MRI suffers from a high false-positive rate in these patients. The imaging community has great opportunities to further investigate and improve existing technologies, better define their strengths and weaknesses, and develop new technologies or new combinations of existing technologies. Because imaging technologies are designed and developed to address a specific clinical task, or a limited ensemble of tasks in detection, characterization, treatment, planning or monitoring of disease and conditions, technology goes through various stages of development, laboratory/ bench testing, clinical testing, calibration and/ or optimization. Likewise, as new technologies—or new applications of existing technologies—are rolled out into clinical practice, clinicians must go through education and training on the operation and functionalities of these systems so that the ensemble human technology will result in maximal quality of patient care. The framework for what is typically referred to as ‘‘Health Technology Assessment’’ has been previously described (1). Such technology assessments are typically performed only after the technology is developed, manufactured, marketed, and introduced into clinical practice (presumably only after some level of evidence has been obtained to demonstrate the ability of the imaging technology to differentiate normal from disease states). All along the way, development, testing, optimization, and calibration will rely on anthropomorphic

Leveraging the digital mammography image screening trial (DMIST) data for the evaluation of computer-aided detection (CAD) devices: a proposal

The availability of the large dataset of screen/film and full-field digital mammograms acquired through the Digital Mammography Imaging Screening Trial (DMIST) presents an extraordinary opportunity for the assessment of CAD devices. The National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering at the U.S. National Institutes of Health have engaged FDA scientists in the development of a plan to leverage this imaging resource to benchmark the performance of current CAD systems. In this talk, we will present an initial proposal for utilizing the DMIST data to quantitatively assess current CAD systems. It is our goal to engage the IWDM community and other interested groups in the development of a consensus on acceptable study designs for this purpose.

Breast Density Estimation: Correlation of Mammographic Density and MR Volumetric Density

Studies have demonstrated a strong correlation between mammographic breast density and breast cancer risk. mammographic breast density may therefore be used as a surrogate marker for monitoring the response to treatment in studies of breast cancer prevention or intervention methods. In this study, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A data set of 37 patients who had corresponding MR images and mammograms was collected. The glandular tissue regions in the MR slices were segmented by a semi-automatic method and the percent glandular tissue volume calculated. mammographic breast density was estimated by an automated image analysis program. It was found that the correlation between the percent dense area of the CC and MLO views and the percent volumetric fibroglandular tissue on MR images was 0.93 and 0.91, respectively, with a mean bias of 4.4%. The high correlation indicates the usefulness of mammographic density as a surrogate for breast density estimation.