Using computer-aided detection in mammography as a decision support

Contains fulltext : 87548.pdf (publisher's version ) (Closed access)OBJECTIVE: To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making. METHODS: A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%. RESULTS: Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant (p = 0.012). Average reading time was 84.7 +/- 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 +/- 57.8 s/case). CONCLUSION: Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.1 oktober 201

Matching mammographic regions in mediolat eral oblique and cranio caudal views: a probabilistic approach

ABSTRACT Most of the current CAD systems detect suspicious mass regions independently in single views. In this paper we present a method to match corresponding regions in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For every possible combination of mass regions in the MLO view and CC view, a number of features are computed, such as the difference in distance of a region to the nipple, a texture similarity measure, the gray scale correlation and the likelihood of malignancy of both regions computed by singleview analysis. In previous research, Linear Discriminant Analysis was used to discriminate between correct and incorrect links. In this paper we investigate if the performance can be improved by employing a statistical method in which four classes are distinguished. These four classes are defined by the combinations of view (MLO/CC) and pathology (TP/FP) labels. We use distance-weighted k-Nearest Neighbor density estimation to estimate the likelihood of a region combination. Next, a correspondence score is calculated as the likelihood that the region combination is a TP-TP link. The method was tested on 412 cases with a malignant lesion visible in at least one of the views. In 82.4% of the cases a correct link could be established between the TP detections in both views. In future work, we will use the framework presented here to develop a context dependent region matching scheme, which takes the number and likelihood of possible alternatives into account. It is expected that more accurate determination of matching probabilities will lead to improved CAD performance

Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses

Item does not contain fulltextOBJECTIVES: We developed a computer-aided detection (CAD) system aimed at decision support for detection of malignant masses and architectural distortions in mammograms. The effect of this system on radiologists' performance depends strongly on its standalone performance. The purpose of this study was to compare the standalone performance of this CAD system to that of radiologists. METHODS: In a retrospective study, nine certified screening radiologists and three residents read 200 digital screening mammograms without the use of CAD. Performances of the individual readers and of CAD were computed as the true-positive fraction (TPF) at a false-positive fraction of 0.05 and 0.2. Differences were analysed using an independent one-sample t-test. RESULTS: At a false-positive fraction of 0.05, the performance of CAD (TPF?=?0.487) was similar to that of the certified screening radiologists (TPF?=?0.518, P?=?0.17). At a false-positive fraction of 0.2, CAD performance (TPF?=?0.620) was significantly lower than the radiologist performance (TPF?=?0.736, P <0.001). Compared to the residents, CAD performance was similar for all false-positive fractions. CONCLUSIONS: The sensitivity of CAD at a high specificity was comparable to that of human readers. These results show potential for CAD to be used as an independent reader in breast cancer screening. KEY POINTS : � Computer-aided detection (CAD) systems are used to detect malignant masses in mammograms � Current CAD systems operate at low specificity to avoid perceptual oversight � A CAD system has been developed that operates at high specificity � The performance of the CAD system is approaching that of trained radiologists � CAD has the potential to be an independent reader in screening