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CASE-ADAPTIVE PROCESSING FOR IMPROVING ACCURACY IN COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER
Title
CASE-ADAPTIVE PROCESSING FOR IMPROVING ACCURACY IN COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER
Creator
Sainz De Cea, Maria Victoria
Date
2018, 2018-05
Description
Breast cancer is the most commonly diagnosed cancer among women (apart from skin cancer) in the US. If detected early, the five-year survival...
Show moreBreast cancer is the most commonly diagnosed cancer among women (apart from skin cancer) in the US. If detected early, the five-year survival rate is 99%. Because of this, early detection of breast cancer has been an extensively studied topic over the years, and screening mammography is the gold standard for this purpose. Microcalcifications (MCs) are tiny calcium deposits that appear as bright spots in mammogram images, and they can be an early sign of breast cancer in asymptomatic women. Computer aided diagnosis (CAD) tools can be used to assist radiologists in detecting MCs and classifying them as benign or malignant. CAD of breast cancer is often hampered by the presence of false positives (FP) among the detected MCs when a reasonable sensitivity level is achieved. The FPs can be caused by MC-like noise, linear structures, etc. Due to the wide range of factors causing FPs, there is a great inter-patient variability, which can degrade the performance of CAD systems. In this work, we aim to reduce the inter-patient variability of CAD systems in order to improve the performance in both MC detection (Computer aided detection or CADe) and classification of MC clusters (Computer aided diagnosis or CADx). The first part of this thesis focuses on MC detection. We first develop a framework for estimating the accuracy in detection of individual MCs within a lesion region. This framework is general and can be applied to any MC detector. The number of FP detections can vary greatly from patient to patient, so having this knowledge will be useful to make decisions in both CADe and CADx systems. Secondly, we present a case-adaptive method for CADe based on Bayes’ risks, where a distribution is fit to the FPs from a mammogram under consideration, based on which the optimal detection threshold is determined for each patient. Finally we present an outlier approach for detection of individual MCs in a lesion region. This approach is based on the fact that individual MCs are usually different from the FPs (brighter, larger in extent), so they can be detected as statistical outliers. The outlier detection is done in a case-by-case basis, which can yield not only a reduction in the number of FPs but also an increase on the uniformity of the detection accuracy among different cases. The second part of the thesis is focused on CADx. We apply the methods developed in the first part to improve the uniformity and performance in the classification of detected lesions as benign or malignant. For this purpose we first present a quality factor approach for adjusting the contribution of the detected individual MCs to the final feature set. Those detections with a higher quality factor can have more impact in the final features, therefore mitigating the effect of the FP detections. Finally, we use the estimated detection accuracy to determine the optimal detection operating threshold. This is shown to boost the CADx performance.
Ph.D. in Electrical Engineering, May 2018
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Simulation, design and applications of a table top analyzer-based phase contrast mammography system
Title
Simulation, design and applications of a table top analyzer-based phase contrast mammography system
Creator
Caudevilla Torras, Oriol
Date
2019
Description
Analyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast...
Show moreAnalyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast methods, phase-contrast modalities measure refraction and scatter properties of the tissue. Such images are particularly suitable for applications such as mammography.The potential advantages of the Analyzer-Based Imaging technology are three fold. First, it shows exceptional contrast when imaging soft tissue, which produces extremely sharp images of the breast compared to absorption images. Second, it provides additional insights about the breast. In particular, the density and scatter images of breast micro-calcifications can help assessing their malignancy better than common mammograms. Third, it has shown potential to reduce the radiation dose deposited in the breast tissue by an order of magnitude compared to common mammography procedures.In the past, Analyzer-Based Imaging has been mainly developed with synchrotron light sources and focused on obtaining micro-resolution images. For such applications, quasi-monoenergetic beams are required. Nevertheless, monochromatic radiation can be easily obtained in synchrotron setups by filtering the source’s spectrum with crystal optics. Since synchrotrons are very brilliant sources, most of their radiation can be filtered out and still obtain low noise phase contrast images. Nowadays, there is a lot of interest in transitioning the technology to a table-top system using compact X-ray sources for mammography. However, compact sources are several orders of magnitude less brilliant, which causes extremely long exposure times. Additionally, the trade-off between exposure time (throughput) and resolution in compact analyzer-based imaging systems is yet to be completely understood.In this thesis, we lay down the principles to develop compact analyzer-based imaging systems capable of imaging a full-sized breast under ten seconds, while ensuring a resolution under 100 microns. This represents a major breakthrough towards obtaining a clinical analyzer-based mammography system. Additionally, we explore a unique application of the analyzer-based technology for breast diagnosis consisting on the assessment of the chemical composition of micro-calcifications. In conjunction with ABI’s unparalleled image quality, determining the chemical composition of micro- calcifications can help to mitigate the high false positive rate in common mammography.
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