Digital Breast Tomosynthesis (DBT) Computer Aided Detection (CAD)


2012 . 11 ~ current







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1.  Introduction

The effects of breast cancers early detection in reducing the mortality rate of that disease have been shown in many clinical studies. Until recently, screening mammography has been considered as the gold standard for breast cancer detection. However the conspicuity of masses is obscured by the presence of overlapping tissue due to projecting three-dimensional (3D) breast information into a two-dimensional a (2D) plane. This inherently limits breast mass detection based on mammograms.


Recently, a new 3D tomographic imaging modality, namely Digital breast tomosynthesis (DBT), has been introduced to alleviate the overlapping tissue problem that can obscure breast masses. In DBT, a series of low-dose projection view images are obtained with an x-ray tube at different angles to the plane of the compressed breast over a limited angular range. A 3D breast volume is reconstructed from the limited number of projection view images using reconstruction algorithms. Although DBT resolves the overlapping tissue problem, the 3D reconstructed volumes suffer from the following:

         The 3D reconstructed volume consists of dozens of slices that have relatively large thickness (e.g., 1~3 mm depending on the tomosynthesis system), on the other hand, the in-plane resolution (parallel to the detector plane) is small (e.g., 0.1 mm 0.1 mm). This problem could lead to an error of the conventional 3D mass detection approaches, which processes 3D information with different resolutions at the same time.

         The limited angular range and the limited number of projection views that are used in the DBT systems cause reconstruction artefacts when reconstructing the 3D volume. Therefore, an object in the out-of-focus plane is blurred, as can be seen in figure 1.

         A larger amount of information in the 3D DBT data should be analyzed by radiologists, which causes a substantial increase in the workload and the possibility for overlooking subtle lesions.





Fig. 1.  Cross sectional view image of an invasive lobular carcinoma. (a) Cross sectional view in the y-z plane. There is the strong reconstruction artefact in this plane. (b) Cross sectional view in the in focus x-y plane, clearly showing the mass lesion. (c) Cross sectional view in the out-of-focus x-y plane, showing a blurred object due to the reconstruction artefact


In this work, to help radiologists detect cancerous efficiently, a novel mass detection approach using slice conspicuity in the 3D reconstructed DBT volumes is proposed to deal with the aforementioned DBT problems with the following considerations:

         The proposed approach solves the limited resolution problem on the quasi-3D domain by employing the detection of regions of interest (ROIs) on each reconstructed slice independently. In parallel, depth directional information is separately utilized to combine the ROIs of reconstructed slices.

         The proposed approach resolves the problem of blur in the out-of-focus planes, by performing feature analysis on the in-focus slices only. To that end, the blurriness of each slice was measuring and selecting in focus slices.


2.  Proposed mass detection in DBT


2.1 Overview


As shown in figure 2, the proposed mass detection consists of the following two phases:

         Segmentation of masses and VOI detection from a given 3D reconstructed digital breast volume

         Feature extraction and classification of the detected VOIs for reducing the number of False Positives (FPs)

(a) Volume of interest (VOI) detection

(b) False positive (FP) reduction

Fig. 2. Overall process of the proposed mass detection method



2.2 Segmentation of masses and VOI detection from a given 3D reconstructed digital breast volume

 Step 1. Preprocessing and mass segmentation:

   For the purpose of increasing the detection performance, a mammographic mass enhancement technique is applied to each reconstructed slice.

   Then, mass regions are segmented by considering the nesting patterns of iso-contours, as well as the gradient between the iso-contours, to characterize mass objects.


Step 2.  Generation of the pooled mass probability map:

 Masses have high correlation between reconstructed slices as they are likely to be detected in the same location in adjacent reconstructed slices. The depth directional correlation between the reconstructed slices to combine the slice information is utilized by considering the following:

       Let   denote as ROI representing the j-th ROI on the i-th reconstructed slice. The mass probability for  can be written as


   A mass probability map on i-th  reconstructed slice that represents the mass saliency of a given location (x, y) is defined as


   we generate a pooled mass probability map  Pooled by averaging mass probability maps over all slices as




Step 3.  ROI selection and grouping: VOI detection:

Figure 3 illustrates how the ROIs are grouped and the mass VOIs are detected. to determine mass candidates, a mass binary map  is obtained by thresholding the pooled mass probability map as follows:


Fig. 3. Illustration of an example for grouping the ROIs and detecting VOIs


 2.3 Feature extraction and classification of the detected VOIs for reducing the number of False Positives (FPs)


 To extract features that well describe the mass lesions, features are extracted only from the central slice; where the mass is most conspicuous. The maximum conspicuous score is calculated by taking the average of the gradient at the edge mass edge margin as shown in the figure 4. The average gradient is calculated according from the gradient image in the margin region shown in figure 4.b.

Fig. 4. Calculating the conspicuously score from each slice. (a) Detected mass (b) Margin region of the detected mass. (c) Gradient magnitude image.



Finally, the features are extracted form mass slices that have a mass conspicuous score above a certain value as shown in figure 5.

Fig. 5. Overall process of the proposed mass detection method


3.   Experiments & Result


Figure 6 shows an example of the detected masses with the proposed method. The first row shows the original ROIs containing a mass, While the second row shows the masses marked with the proposed VOI detection method. As a reference, the last row shows the masses with the outlines manually marked by radiologists.

Fig. 6. Examples of detected masses with the proposed method



To evaluate the proposed detection method, FROC curve was used. As can be seen in the FROC curve shown below, the sensitivity of the proposed VOI detection method is higher than the sensitivity of the commonly used 3D multilevel thersholding method for all false positive rates.

Fig. 7. Detection performance comparison using FROC curve


Moreover, the proposed feature extraction method using the most conspicuous slice performs better than conventional 3D features as can be seen in the ROC curve shown below. It should be noted that the sensitivity of the detection further improves when combining the proposed features with conventional features extracted from the most conspicuous slice.

Fig. 8. Effectiveness of the proposed conspicuous slice pooling based features extraction method using ROC curves



Contact Person: Prof. Yong Man Ro (

1.     Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro, Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes, Physics in Medicine and Biology, vol. 59, pp. 5003-5023, 2014.