Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Cellular and Molecular Biomarkers for Risk Assessment of Colorectal cancer

The detection of Colorectal Cancer (CRC), at early stages, is one of the proven strategies resulting in a higher cure rate. In recent years, several studies have appeared identifying potential cancer markers in serum, plasma and stool in an attempt to improve actual screening procedures. Thus, the aim of the study was (1) Evaluate MN frequency, (2) Evaluate plasma ultrafiltrate capacity to induce MN formation, (3) Evaluate SEPT9 and NOTCH3 promoter methylation profile in peripheral blood lymphocytes from subjects resulted positive to fecal occult blood test and examined by colonoscopy. MN frequency was significantly higher in subjects with histological diagnosis of CRC and adenoma than control (p ≤ 0.001 and p ≤ 0.01, respectively). About, CF-MN analysis, a statistically significant difference was observed between CRC and control (p ≤ 0.05). On the other hand, SEPT9 and NOTCH3 promoter methylation status was significantly lower in CRC subjects than controls; additionally, NOTCH3 promoter methylation status was significantly lower in CRC subjects than adenoma subjects (p ≤ 0.01). The results obtained allow conclude that MN frequency varies according CRC pathologic status and, together with other variables, is a valid biomarker for adenoma and CRC risk. Additionally, the plasma of patients affected with CRC not only serve as a biomarker for oxidative stress but also as biomarker of genetic damage correlated with the carcinogenic process that verifies in colon-rectum. SEPT9 and NOTCH3 promoter methylation status, at peripheral blood level, varies according hystopathological changes observed in colon-rectum, suggesting that promoter methylation profile of these genes could be a reliable biomarker for CRC risk.