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

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134897/1/mp2072.pd

Computerâ aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134920/1/mp0955.pd

Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134871/1/mp8694.pd

Breast Tomosynthesis: Aspects on detection and perception of simulated lesions

The aim of this thesis was to investigate aspects on detectability of simulated lesions (microcalcifications and masses) in digital mammography (DM) and breast tomosynthesis (BT). Perception in BT image volumes were also investigated by evaluating certain reading conditions. The first study concerned the effect of system noise on the detection of masses and microcalcification clusters in DM images using a free-response task. System noise has an impact on image quality and is related to the dose level. It was found to have a substantial impact on the detection of microcalcification clusters, whereas masses were relatively unaffected. The effect of superimposed tissue in DM is the major limitation hampering the detection of masses. BT is a three-dimensional technique that reduces the effect of superimposed tissue. In the following two studies visibility was quantified for both imaging modalities in terms of the required contrast at a fixed detection performance (92% correct decisions). Contrast detail plots for lesions with sizes 0.2, 1, 3, 8 and 25 mm were generated. The first study involved only an in-plane BT slice, where the lesion centre appeared. The second study repeated the same procedure in BT image volumes for 3D distributed microcalcification clusters and 8 mm masses at two dose levels. Both studies showed that BT needs substantially less contrast than DM for lesions above 1 mm. Furthermore, the contrast threshold increased as the lesion size increased for both modalities. This is in accordance with the reduced effect of superimposed tissue in BT. For 0.2 mm lesions, substantially more contrast was needed. At equal dose, DM was better than BT for 0.2 mm lesions and microcalcification clusters. Doubling the dose substantially improved the detection in BT. Thus, system noise has a substantial impact on detection. The final study evaluated reading conditions for BT image volumes. Four viewing procedures were assessed: free scroll browsing only or combined with initial cine loops at frame rates of 9, 14 and 25 fps. They were viewed on a wide screen monitor placed in vertical or horizontal positions. A free-response task and eye tracking were utilized to record the detection performance, analysis time, visual attention and search strategies. Improved reading conditions were found for horizontally aligned BT image volumes when using free scroll browsing only or combined with a cine loop at the fastest frame rate

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Determinants and influence of mammographic features on breast cancer risk

Mammographic density and mammographic microcalcifications are the key imaging features in mammography examination. Mammographic density is known as a strong risk factor for breast cancer and is the radiographic appearance of epithelial and fibrous tissue which appears white on a mammogram. While, the dark part of a mammogram represents the fatty tissue. Mammographic microcalcifications appear as small deposits of calcium and they are one of the earliest sign of breast cancer. Malignant microcalcifications are seen in both in situ and invasive lesions. In this thesis we used the data from the prospective KARMA cohort to study the association between established breast cancer risk factors with mammographic density change over time (Study I), to examine the association between annual mammographic density change and risk of breast cancer (Study II), to investigate the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry (Study III), and finally to elucidate the association between microcalcification clusters, their asymmetry, and risk of overall and subtype specific breast cancer (Study IV). The lifestyle and reproductive factors were assessed using web-based questionnaires. Average mammographic density and total microcalcification clusters were measured using a Computer Aided Detection system (CAD) and the STRATUS method, respectively. In Study I, the average yearly dense area change was -1.0 cm . Body mass index (BMI) and physical activity were statistically associated with density change. Beside age, lean and physically active women had the largest decrease in mammographic density per year. In Study II, overall, 563 women were diagnosed with breast cancer and annual mammographic density change did not seem to influence the risk of breast cancer. Furthermore, density change does not seem to modify the association between baseline density and risk of breast cancer. In Study III, age, mammographic density, genetic factors related to breast cancer, having more children, longer duration of breast-feeding were significantly associated with increased risk of presence of microcalcification clusters. In Study IV, 676 women were diagnosed with breast cancer. Further, women with 33 microcalcification clusters had 2 times higher risk of breast cancer compared to women with no clusters. Microcalcification clusters were associated with both in situ and invasive breast cancer. Finally, during postmenopausal period, microcalcification clusters influence risk of breast cancer to the similar extend as baseline mammographic density. In conclusion, we have identified novel determinants of mammographic density changes and potential predictors of suspicious mammographic microcalcification clusters. Further, our results suggested that annual mammographic density change does not influence breast cancer risk, while presence of suspicious microcalcification clusters was strongly associated with breast cancer risk