Computer-Aided Microcalcification Detection on Digital Breast Tomosynthesis Data: A Preliminary Evaluation

Bernard, Sylvain; Muller, Serge; Onativia, Jon

doi:10.1007/978-3-540-70538-3_22

Cited by 16 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[25][26][27][28][29] Compared with the detection of masses, studies investigating the detection of microcalcification clusters on DBT are more preliminary. [30][31][32][33] Reiser et al 30 investigated the detection of microcalcifications on individual PVs. The detected microcalcifications were then backprojected into the 3D volume.…”

Section: Introductionmentioning

confidence: 99%

“…On a data set of 30 image sets with microcalcification clusters and 30 image sets without visible findings, their detection method achieved a sensitivity of 86% with 1.3 false-positive (FP) clusters per DBT volume. Bernard et al 31 developed an algorithm for the detection of microcalcification clusters on filtered back-projection reconstructed slices. The voxel contrast was enhanced by convolving the image volume with a Mexican hat wavelet at a specific scale.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

et al. 2011

View full text Add to dashboard Cite

Purpose: To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system. Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs. Results: The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters. Conclusions: Our study indicates the feasibility of the 3D approach to the detection of clustered microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Sahiner et al [3] proposed a system for detecting clustered microcalcifications from 3D volumes in a 3D approach. In another study, Bernard et al [4] proposed a method for microcalcification detection from filtered back-projection reconstructed slices with a Mexican hat wavelet based enhancement. Though, many research efforts have been made for efficiently detecting microcalcifications in DBT, false-positive (FP) rate is still high to be useful in clinical practice [3].…”

Section: Introductionmentioning

confidence: 99%

Improvement of subtle microcalcifications detection in DBT slices

Kim

Suk

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

View full text Add to dashboard Cite

In this paper, a novel microcalcification detection in Digital Breast Tomosynthesis (DBT) is proposed, which allows detecting subtle microcalcifications that have relatively low contrast in DBT slices. 3D Foveal segmentation is employed for volume of interest (VOI) detection using adaptive thresholding scheme by local volume contrast. Furthermore, in order to conserve structural characteristics of clustered microcalcifications, features from VOI maximum ray-tracing images with three orthogonal directions are extracted. Extensive and comparative experiments were carried out to demonstrate the effectiveness of the proposed 3D Foveal segmentation and the feature extraction method on clinical DBT cases. The results showed that the proposed microcalcification detection achieves a sensitivity of 98% at 0.5 FP per volume.

show abstract

“…Computerized lesion detection in DBT can be performed in the multiple PV images, the tomosynthesized volume, or a combination of both. Bernard et al 3 performed microcalcification detection in FBP-reconstructed slices. Reiser et al 4 performed microcalcification detection on the PV images before reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Digital breast tomosynthesis: computerized detection of microcalcifications in reconstructed breast volume using a 3D approach

Chan¹,

Sahiner

Wei

et al. 2010

SPIE Proceedings

View full text Add to dashboard Cite

We are developing a computer-aided detection (CAD) system for clustered microcalcifications in digital breast tomosynthesis (DBT). In this preliminary study, we investigated the approach of detecting microcalcifications in the tomosynthesized volume. The DBT volume is first enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices with a calcification response function and signal-to-noise ratio enhancement filtering. Potential signal sites are identified in the enhanced volume and local analysis is performed to further characterize each object. A 3D dynamic clustering procedure is designed to locate potential clusters using hierarchical criteria. We collected a pilot data set of two-view DBT mammograms of 39 breasts containing microcalcification clusters (17 malignant, 22 benign) with IRB approval. A total of 74 clusters were identified by an experienced radiologist in the 78 DBT views. Our prototype CAD system achieved view-based sensitivity of 90% and 80% at an average FP rate of 7.3 and 2.0 clusters per volume, respectively. At the same levels of case-based sensitivity, the FP rates were 3.6 and 1.3 clusters per volume, respectively. For the subset of malignant clusters, the view-based detection sensitivity was 94% and 82% at an average FP rate of 6.0 and 1.5 FP clusters per volume, respectively. At the same levels of case-based sensitivity, the FP rates were 1.2 and 0.9 clusters per volume, respectively. This study demonstrated that computerized microcalcification detection in 3D is a promising approach to the development of a CAD system for DBT. Study is underway to further improve the computer-vision methods and to optimize the processing parameters using a larger data set.

show abstract

Computer-Aided Microcalcification Detection on Digital Breast Tomosynthesis Data: A Preliminary Evaluation

Cited by 16 publications

References 5 publications

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Improvement of subtle microcalcifications detection in DBT slices

Digital breast tomosynthesis: computerized detection of microcalcifications in reconstructed breast volume using a 3D approach

Contact Info

Product

Resources

About