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

A Longitudinal Study of Mammograms Utilizing the Automated Wavelet Transform Modulus Maxima Method

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time a tumor is detected, it has already progressed to a deadly stage. Unlike previous research, this paper focuses on the predetection of tumorous tissue. This novel approach sets out to examine changes in the breast microenvironment instead of locating and identifying tumors. The purpose of this paper is to explore whether it is possible to discover changes in the breast tissue microenvironment which later develop into breast cancer. We hypothesized that changes in the breast tissue would be detected by analyzing mammograms from the years prior to the discovery of tumorous tissue by a radiologist. We analyzed a set of time-series digital mammograms corresponding to 26 longitudinal cancer cases, obtained through a collaboration with Eastern Maine Medical Center (EMMC) in Bangor, Maine. We automated the Wavelet Transform Modulus Maxima (WTMM) method, a mathematical formalism that we used to perform a multifractal analysis. In particular, this automated WTMM (AWTMM) was used to calculate the Hurst exponent, a metric that is correlated with breast tissue density. The AWTMM allowed us to see with greater detail the changes in mammogram tissue, specifically concerning breast density. The results suggest that signs of malignancy can be observed as early as two years before standard radiological procedures. In this research, we identify a set of variables that show significance when classifying precancerous tissue

Wavelet-based tracking of bacteria in unreconstructed off-axis holograms

We propose an automated wavelet-based method of tracking particles in unreconstructed off-axis holograms to provide rough estimates of the presence of motion and particle trajectories in digital holographic microscopy (DHM) time series. The wavelet transform modulus maxima segmentation method is adapted and tailored to extract Airy-like diffraction disks, which represent bacteria, from DHM time series. In this exploratory analysis, the method shows potential for estimating bacterial tracks in low-particle-density time series, based on a preliminary analysis of both living and dead Serratia marcescens, and for rapidly providing a single-bit answer to whether a sample chamber contains living or dead microbes or is empty

Monte Carlo modelling of Raman scattering in heterogeneous breast tissue

Breast cancer is the most common cancer for a woman to develop in her lifetime. By detecting breast cancer at an early stage, the symptoms can be easier to manage and the patient should have the best chance of survival. The current gold standard for breast cancer detection is a mammogram, followed by a biopsy and histopathology. This is effective but can also be expensive and invasive. A promising addition to the diagnostic pathway uses vibrational spectroscopy which utilises non-elastic interactions between light and tissue. Raman spectroscopy has been used widely in industry and research: it is a non-invasive and chemically specific technique. This spectroscopic technique has been proven to be applicable to the detection of microcalcifications in breast tissue to aid in diagnosing breast cancer and potentially reducing the number of biopsies required. This thesis involves the development of algorithms to model Raman scattering in biological tissues to aid in the improvement of breast cancer detection. The technique used is the numerical modelling method Monte Carlo Radiative Transport (MCRT) to effectively simulate the transport of light through turbid media. There is a need for a fast and flexible code capable of modelling a variety of Raman source materials, tissue types and shapes, input laser beams and detectors. This rapid simulation of light transport through breast tissue can provide more information and insight to complement the practical measurements and analysis of experimental work, which can be used to improve future experiments and probes. By implementing physically correct Raman scattering into a fast and powerful code, and utilising work from the field to estimate the optical properties of tissues, simulations to supplement experimental work and predict potential clinical results are performed and analysed

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection

Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

Machine learning methods for the analysis and interpretation of images and other multi-dimensional data

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