Multiview-based computer-aided detection scheme for breast masses
Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213, USA. Medical Physics
(Impact Factor: 2.64).
09/2006; 33(9):3135-43. DOI: 10.1118/1.2237476
In this study, we developed and tested a new multiview-based computer-aided detection (CAD) scheme that aims to maintain the same case-based sensitivity level as a single-image-based scheme while substantially increasing the number of masses being detected on both ipsilateral views. An image database of 450 four-view examinations (1800 images) was assembled. In this database, 250 cases depicted malignant masses, of which 236 masses were visible on both views and 14 masses were visible only on one view. First, we detected suspected mass regions depicted on each image in the database using a single-image-based CAD. For each identified region (with detection score > or = 0.55), we then identified a matching strip of interest on the ipsilateral view based on the projected distance to the nipple along the centerline. By lowering CAD operating threshold inside the matching strip, we searched for a region located inside the strip and paired it with the original region. A multifeature-based artificial neural network scored the likelihood of the paired "matched" regions representing true-positive masses. All single (unmatched) regions except for those either with very high detection scores (> or = 0.85) or those located near the chest wall that cannot be matched on the other view were discarded. The original single-image-based CAD scheme detected 186 masses (74.4% case-based sensitivity) and 593 false-positive regions. Of the 186 identified masses, 91 were detected on two views (48.9%) and 95 were detected only on one view (51.1%). Of the false-positive detections, 54 were paired on the ipsilateral view inside the corresponding matching strips and the remaining 485 were not, which represented 539 case-based false-positive detections (0.3 per image). Applying the multiview-based CAD scheme, the same case-based sensitivity was maintained while cueing 169 of 186 masses (90.9%) on both views and at the same time reducing the case-based false-positive detection rate by 23.7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate.
Available from: Bin Zheng
- "For each case, we computed bilateral mammographic density asymmetry by selecting two CC view images acquired from both the left and right breast. First, we applied a predeveloped computing algorithm (Zheng et al 2006b) to segment breast tissue area by assuming that a transition curve with the smoothest curvature between breast tissue and air background represents the segmentation boundary (skin–air interfaces). For this purpose, an iterative searching method was applied to detect the smoothest curvature between breast tissue and the air background. "
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ABSTRACT: Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists' detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on 'high-risk' cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on 'low-risk' cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in 'current' examination and missed in 'prior' examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on 'current' examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on 'prior' examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the 'prior' examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.
Available from: Maurice Samulski
- "However, finding the corresponding location of a given region in the ipsilateral view can be a difficult task and in multiview CAD systems it is an essential step that needs to be automated. Several groups have worked on this topic in 2D mammography (Paquerault et al 2002, Altrichter et al 2005, Zheng et al 2006, Samulski and Karssemeijer 2008). In 2D projection mammography, the depth of a region is unknown, which results in a large uncertainty when looking for the same region in the ipsilateral view. "
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ABSTRACT: To improve cancer detection in mammography, breast examinations usually consist of two views per breast. In order to combine information from both views, corresponding regions in the views need to be matched. In 3D digital breast tomosynthesis (DBT), this may be a difficult and time-consuming task for radiologists, because many slices have to be inspected individually. For multiview computer-aided detection (CAD) systems, matching corresponding regions is an essential step that needs to be automated. In this study, we developed an automatic method to quickly estimate corresponding locations in ipsilateral tomosynthesis views by applying a spatial transformation. First we match a model of a compressed breast to the tomosynthesis view containing a point of interest. Then we estimate the location of the corresponding point in the ipsilateral view by assuming that this model was decompressed, rotated and compressed again. In this study, we use a relatively simple, elastically deformable sphere model to obtain an analytical solution for the transformation in a given DBT case. We investigate three different methods to match the compression model to the data by using automatic segmentation of the pectoral muscle, breast tissue and nipple. For validation, we annotated 208 landmarks in both views of a total of 146 imaged breasts of 109 different patients and applied our method to each location. The best results are obtained by using the centre of gravity of the breast to define the central axis of the model, around which the breast is assumed to rotate between views. Results show a median 3D distance between the actual location and the estimated location of 14.6 mm, a good starting point for a registration method or a feature-based local search method to link suspicious regions in a multiview CAD system. Approximately half of the estimated locations are at most one slice away from the actual location, which makes the method useful as a mammographic workstation tool for radiologists to interactively find corresponding locations in ipsilateral tomosynthesis views.
Available from: Maurice Samulski
- "Most of the matching approaches are therefore based on using a set of landmarks, such as the location of the nipple and points on the pectoral muscle boundary. Two main methods of triangulating a lesion in two projections have been described in textbooks and journal articles , , , , –: the arc-based method and the straight-line based method. The arc-based method is based on the idea that the distance between the nipple and lesion remains fairly constant during breast compression, which may be explained by the fact that mammographers pull away the breast from the chest wall for optimal positioning. "
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ABSTRACT: When reading mammograms, radiologists combine information from multiple views to detect abnormalities. Most computer-aided detection (CAD) systems, however, use primitive methods for inclusion of multiview context or analyze each view independently. In previous research it was found that in mammography lesion-based detection performance of CAD systems can be improved when correspondences between MLO and CC views are taken into account. However, detection at case level detection did not improve. In this paper, we propose a new learning method for multiview CAD systems, which is aimed at optimizing case-based detection performance. The method builds on a single-view lesion detection system and a correspondence classifier. The latter provides class probabilities for the various types of region pairs and correspondence features. The correspondence classifier output is used to bias the selection of training patterns for a multiview CAD system. In this way training can be forced to focus on optimization of case-based detection performance. The method is applied to the problem of detecting malignant masses and architectural distortions. Experiments involve 454 mammograms consisting of four views with a malignant region visible in at least one of the views. To evaluate performance, five-fold cross validation and FROC analysis was performed. Bootstrapping was used for statistical analysis. A significant increase of case-based detection performance was found when the proposed method was used. Mean sensitivity increased by 4.7% in the range of 0.01-0.5 false positives per image.
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