Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

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

Computer-aided image quality assessment in automated 3D breast ultrasound images

Automated 3D breast ultrasound (ABUS) is a valuable, non-ionising adjunct to X-ray mammography for breast cancer screening and diagnosis for women with dense breasts. High image quality is an important prerequisite for diagnosis and has to be guaranteed at the time of acquisition. The high throughput of images in a screening scenario demands for automated solutions. In this work, an automated image quality assessment system rating ABUS scans at the time of acquisition was designed and implemented. Quality assessment of present diagnostic ultrasound images has rarely been performed demanding thorough analysis of potential image quality aspects in ABUS. Therefore, a reader study was initiated, making two clinicians rate the quality of clinical ABUS images. The frequency of specific quality aspects was evaluated revealing that incorrect positioning and insufficiently applied contact fluid caused the most relevant image quality issues. The relative position of the nipple in the image, the acoustic shadow caused by the nipple as well as the shape of the breast contour reflect patient positioning and ultrasound transducer handling. Morphological and histogram-based features utilized for machine learning to reproduce the manual classification as provided by the clinicians. At 97 % specificity, the automatic classification achieved sensitivities of 59 %, 45 %, and 46 % for the three aforementioned aspects, respectively. The nipple is an important landmark in breast imaging, which is generally---but not always correctly---pinpointed by the technicians. An existing nipple detection algorithm was extended by probabilistic atlases and exploited for automatic detection of incorrectly annotated nipple marks. The nipple detection rate was increased from 82 % to 85 % and the classification achieved 90 % sensitivity at 89 % specificity. A lack of contact fluid between transducer and skin can induce reverberation patterns and acoustic shadows, which can possibly obscure lesions. Parameter maps were computed in order to localize these artefact regions and yielded a detection rate of 83 % at 2.6 false positives per image. Parts of the presented work were integrated to clinical workflow making up a novel image quality assessment system that supported technicians in their daily routine by detecting images of insufficient quality and indicating potential improvements for a repeated scan while the patient was still in the examination room. First evaluations showed that the proposed method sensitises technicians for the radiologists' demands on diagnostically valuable images

Functional Magnetic Resonance Imaging of Breast Cancer

This thesis examines the use of magnetic resonance imaging (MRI) techniques in the detection of breast cancer and the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NACT). This thesis compares the diagnostic performance of diffusion-weighted imaging (DWI) models in the breast using a systematic review and meta-analysis. Advanced diffusion models have been proposed that may improve the performance of standard DWI using the apparent diffusion coefficient (ADC) to discriminate between malignant and benign breast lesions. Pooling the results from 73 studies, comparable diagnostic accuracy is shown using the ADC and parameters from the intra-voxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) models. This work highlights a lack of standardisation in DWI protocols and methodology. Conventional acquisition techniques used in DWI often suffer from image artefacts and low spatial resolution. A multi-shot DWI technique, multiplexed sensitivity encoding (MUSE), can improve the image quality of DWI. A MUSE protocol has been optimised through a series of phantom experiments and validated in 20 patients. Comparing MUSE to conventional DWI, statistically significant improvements are shown in distortion and blurring metrics and qualitative image quality metrics such as lesion conspicuity and diagnostic confidence, increasing the clinical utility of DWI. This thesis investigates the use of dynamic contrast-enhanced MRI (DCE-MRI) in the detection of breast cancer and the prediction of pCR. Abbreviated MRI (ABB-MRI) protocols have gained increasing attention for the detection of breast cancer, acquiring a shortened version of a full diagnostic protocol (FDP-MRI) in a fraction of the time, reducing the cost of the examination. The diagnostic performance of abbreviated and full diagnostic protocols is systematically compared using a meta-analysis. Pooling 13 studies, equivalent diagnostic accuracy is shown for ABB-MRI in cohorts enriched with cancers, and lower but not significantly different diagnostic performance is shown in screening cohorts. Higher order imaging features derived from pre-treatment DCE-MRI could be used to predict pCR and inform decisions regarding targeted treatment, avoiding unnecessary toxicity. Using data from 152 patients undergoing NACT, radiomics features are extracted from baseline DCE-MRI and machine learning models trained to predict pCR with moderate accuracy. The stability of feature selection using logistic regression classification is demonstrated and a comparison of models trained using features from different time points in the dynamic series demonstrates that a full dynamic series enables the most accurate prediction of pCR.GE Healthcare funded PhD Studentshi

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models