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

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

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.pd

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Measurement of Tumor Extent and Effects of Breast Compression in Digital Mammography and Breast Tomosynthesis

Breast cancer is the most common form of cancer affecting women in the western countries. Today x-ray digital mammography (DM) of the breast is commonly used for early detection of breast cancer. However, the sensitivity of mammography is limited, mainly due to the fact that a 3D volume is projected down to a 2D image. This problem can be partially solved by a tomographic technique. Breast tomosynthesis (BT) reduces the detrimental effect of the projected anatomy. Tumor size is an important predictor of prognosis and treatment effect. We hypothesized that the tumor outline would be better defined in BT and therefore tumor measurement in BT would be more accurate compared with DM. The results showed that breast tumor size measured on BT correlated better with the size measured by the pathologists on the surgical specimens compared with measurement on DM. Breast compression is important in mammography both to improve image quality and to reduce the radiation dose to the breast, but it also has a negative consequence as some women refrain from mammography due to the pain associated with the examination. Since BT is a 3D technique, it was hypothesized that less breast compression force can be applied. The results indicated that less compression force is possible without significantly compromising the diagnostic quality of the image and that the patient comfort was improved. An applied breast compression force as used in mammography results in a pressure distribution over the breast. The pressure distribution was assessed using thin pressure sensors attached to the compression plate. The results showed that the pressure distribution was heterogeneous in appearance and varied widely between different breasts. In almost half of the subjects most of the pressure was over the juxtathoracic part of the breast and the pectoral muscle with little or no pressure over the rest of the breast. Another concern regarding breast compression is the question whether the resulting pressure might damage tumors, causing a shedding of malignant cells into the blood system. Peripheral venous blood samples were drawn before and after breast compression and analyzed for circulating tumor cells. The study found no elevated number of circulating cancer cells in peripheral blood after breast compression. Future analysis of samples from veins draining the breast are needed to study if circulating tumor cells are being trapped in the lung capillaries

MAMMOGRAM AND TOMOSYNTHESIS CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Mammography is the most widely used method of screening for breast cancer. Traditional mammography produces two-dimensional X-ray images, while advanced tomosynthesis mammography produces reconstructed three-dimensional images. Due to high variability in tumor size and shape, and the low signal-to-noise ratio inherent to mammography, manual classification yields a significant number of false positives, thereby contributing to an unnecessarily large number of biopsies performed to reduce the risk of misdiagnosis. Achieving high diagnostic accuracy requires expertise acquired over many years of experience as a radiologist. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. The convolutional process involves simplifying an image containing millions of pixels to a set of small feature maps, thereby reducing the input dimension while retaining the features that distinguish different classes of images. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, high-quality original mammograms and tomosynthesis were obtained with approval from an institutional review board. Different classifiers based on convolutional neural networks were built to classify the 2-D mammograms and 3-D tomosynthesis, and each classifier was evaluated based on its performance relative to truth values generated by a board of expert radiologists. The results show that CNNs have great potential for automatic breast cancer detection using mammograms and tomosynthesis

Detection and diagnosis of breast lesions: Performance evaluation of digital breast tomosynthesis and magnetic resonance mammography

AbstractObjectiveTo assess the impact of digital breast tomosynthesis (DBT) and magnetic resonance mammography (MRM) in enhancing the performance of digital mammography (DM) in the detection and evaluation of different breast lesions.Patients and methodsIn this retrospective study, 98 patients with 103 breast lesions were assessed by DM, DBT and MRM. Mammography images were acquired using the “combo mode", where both DM and DBT scanned in the same compression. MRM was performed by 1T open system. Each lesion was assigned a blinded category in an individual performance for each modality. The resultant BI-RADS categories were correlated with reports of the pathology specimens or outcome of 18-month follow-up.ResultsBoth DBT and MRM showed equivalent sensitivity of 92%. The specificity for DBT and MRM was 80.7% and 89.7% respectively. The efficacy of DM was raised from 61% to 83.5% with DBT and 90.2% with MRM. The results of the three modalities and the final diagnosis revealed a significant correlation (p=0.035).The association between the results of DBT and those of MRM showed statistically significant difference between DBT and MRM for diagnosing breast lesions (p=0.001).ConclusionBoth MRM and DBT provide better performance than classic DM. Adding either of these modalities to the classic examination enhances diagnosis and precise disease distribution

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy