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

Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning

Breast cancer is the most common cancer among women in the world. Nevertheless, early detection of breast cancer improves the chance of successful treatment. Digital breast tomosynthesis (DBT) as a new tomographic technique was developed to minimize the limitations of conventional digital mammography screening. A DBT is a quasi-three-dimensional image that is reconstructed from a small number of two-dimensional (2D) low-dose X-ray images. The 2D X-ray images are acquired over a limited angular around the breast. Our research aims to introduce computer-aided detection (CAD) frameworks to detect early signs of breast cancer in DBTs. In this thesis, we propose three CAD frameworks for detection of breast cancer in DBTs. The first CAD framework is based on hand-crafted feature extraction. Concerning early signs of breast cancer: mass, micro-calcifications, and bilateral asymmetry between left and right breast, the system includes three separate channels to detect each sign. Next two CAD frameworks automatically learn complex patterns of 2D slices using the deep convolutional neural network and the deep cardinality-restricted Boltzmann machines. Finally, the CAD frameworks employ a multiple-instance learning approach with randomized trees algorithm to classify DBT images based on extracted information from 2D slices. The frameworks operate on 2D slices which are generated from DBT volumes. These frameworks are developed and evaluated using 5,040 2D image slices obtained from 87 DBT volumes. We demonstrate the validation and usefulness of the proposed CAD frameworks within empirical experiments for detecting breast cancer in DBTs

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Design, development and use of a deformable breast phantom to assess the relationship between thickness and lesion visibility in full field digital mammography

Aim of research:This research aimed to design and develop a synthetic anthropomorphic breast phantom with cancer mimicking lesions and use this phantom to assess the relationship between lesion visibility and breast thickness in mammography. Due to the risk of cancer induction associated with the use of ionising radiation on breast tissues, experiments on human breast tissue was not practical. Therefore, a synthetic anthropomorphic breast phantom with cancer mimicking lesions was needed to be designed and developed in order to provide a safe platform to evaluate the relationship between lesion visibility and breast thickness in mammography. Method: As part of this research custom Polyvinyl alcohol (PVAL) breast phantoms with embedded PVAL lesions doped with contrast agent were fabricated and utilised. These breast phantoms exhibited mechanical and X-ray properties which were similar to female breast/breast cancer tissues. In order for this research to be useful for human studies, patient safety factors have constrained the extent of this research. These factors include compression force and radiation dose. After acquiring mammograms of phantoms with varying thicknesses, the image quality of the embedded lesions were evaluated both perceptually and mathematically.The two-alternative forced choice (2AFC) perceptual method was used to evaluate image quality of the lesions. For mathematical evaluation the following methods were utilised: line profile analysis, contrast-to noise ratio (CNR), signal-to noise ratio (SNR) and figure of merit (FOM).Results: The results of the visual perception analysis of the mammograms demonstrate that as breast compressed thickness reduces the image quality increases. Additionally, the results display a correlation in the reduction in the level of noise with the reduction in breast thickness. This noise reduction was also demonstrated in the profile plots of the lesions. The line profile analysis, in agreement with visual perception, shows improvement of sharpness of the lesion edge in relation to the reduction of the phantom thickness. The intraclass correlation coefficient (ICC) has shown a great consistency and agreement among the observers for visibility, sharpness, contrast and noise. The ICC results are not as conclusive for the size criterion. Mathematical evaluation results also show a correlation of improvement in the image quality with the reduction in breast thickness. The results show that for the measures CNR, SNR, and FOM, the increase in image quality has a threshold after which the image quality ceases to improve and instead begins to reduce. CNR and FOM dropped when the breast phantom thickness was reduced approximately 40% of its initial thickness. This consistently happened at the point where the filter changed from rhodium (Rh) to molybdenum (Mo). Conclusion: This breast phantom study successfully designed and developed an anthropomorphic compressible breast phantom with cancer mimicking lesions with mechanical and X-ray properties similar to human breast tissue. This study also demonstrates that as breast compressed thickness reduces the visibility of the perceived lesion increases. The radiation dose generally decreases up to the point that the filter changes from rhodium to molybdenum. After this point, the radiation dose increases regardless of the phantom thickness. The results from this thesis are likely to have implications for clinical practice, as they support the need for compression/thickness reduction to enhance lesion visibilit