A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Analyzing the breast tissue in mammograms using deep learning

La densitat mamogràfica de la mama (MBD) reflecteix la quantitat d'àrea fibroglandular del teixit mamari que apareix blanca i brillant a les mamografies, comunament coneguda com a densitat percentual de la mama (PD%). El MBD és un factor de risc per al càncer de mama i un factor de risc per emmascarar tumors. Tot i això, l'estimació precisa de la DMO amb avaluació visual continua sent un repte a causa del contrast feble i de les variacions significatives en els teixits grassos de fons en les mamografies. A més, la interpretació correcta de les imatges de mamografia requereix experts mèdics altament capacitats: És difícil, laboriós, car i propens a errors. No obstant això, el teixit mamari dens pot dificultar la identificació del càncer de mama i associar-se amb un risc més gran de càncer de mama. Per exemple, s'ha informat que les dones amb una alta densitat mamària en comparació amb les dones amb una densitat mamària baixa tenen un risc de quatre a sis vegades més gran de desenvolupar la malaltia. La clau principal de la computació de densitat de mama i la classificació de densitat de mama és detectar correctament els teixits densos a les imatges mamogràfiques. S'han proposat molts mètodes per estimar la densitat mamària; no obstant això, la majoria no estan automatitzats. A més, s'han vist greument afectats per la baixa relació senyal-soroll i la variabilitat de la densitat en aparença i textura. Seria més útil tenir un sistema de diagnòstic assistit per ordinador (CAD) per ajudar el metge a analitzar-lo i diagnosticar-lo automàticament. El desenvolupament actual de mètodes daprenentatge profund ens motiva a millorar els sistemes actuals danàlisi de densitat mamària. L'enfocament principal de la present tesi és desenvolupar un sistema per automatitzar l'anàlisi de densitat de la mama ( tal com; Segmentació de densitat de mama (BDS), percentatge de densitat de mama (BDP) i classificació de densitat de mama (BDC) ), utilitzant tècniques d'aprenentatge profund i aplicant-la a les mamografies temporals després del tractament per analitzar els canvis de densitat de mama per trobar un pacient perillós i sospitós.La densidad mamográfica de la mama (MBD) refleja la cantidad de área fibroglandular del tejido mamario que aparece blanca y brillante en las mamografías, comúnmente conocida como densidad porcentual de la mama (PD%). El MBD es un factor de riesgo para el cáncer de mama y un factor de riesgo para enmascarar tumores. Sin embargo, la estimación precisa de la DMO con evaluación visual sigue siendo un reto debido al contraste débil y a las variaciones significativas en los tejidos grasos de fondo en las mamografías. Además, la interpretación correcta de las imágenes de mamografía requiere de expertos médicos altamente capacitados: Es difícil, laborioso, caro y propenso a errores. Sin embargo, el tejido mamario denso puede dificultar la identificación del cáncer de mama y asociarse con un mayor riesgo de cáncer de mama. Por ejemplo, se ha informado que las mujeres con una alta densidad mamaria en comparación con las mujeres con una densidad mamaria baja tienen un riesgo de cuatro a seis veces mayor de desarrollar la enfermedad. La clave principal de la computación de densidad de mama y la clasificación de densidad de mama es detectar correctamente los tejidos densos en las imágenes mamográficas. Se han propuesto muchos métodos para la estimación de la densidad mamaria; sin embargo, la mayoría de ellos no están automatizados. Además, se han visto gravemente afectados por la baja relación señal-ruido y la variabilidad de la densidad en apariencia y textura. Sería más útil disponer de un sistema de diagnóstico asistido por ordenador (CAD) para ayudar al médico a analizarlo y diagnosticarlo automáticamente. El desarrollo actual de métodos de aprendizaje profundo nos motiva a mejorar los sistemas actuales de análisis de densidad mamaria. El enfoque principal de la presente tesis es desarrollar un sistema para automatizar el análisis de densidad de la mama ( tal como; Segmentación de densidad de mama (BDS), porcentaje de densidad de mama (BDP) y clasificación de densidad de mama (BDC)), utilizando técnicas de aprendizaje profundo y aplicándola en las mamografías temporales después del tratamiento para analizar los cambios de densidad de mama para encontrar un paciente peligroso y sospechoso.Mammographic breast density (MBD) reflects the amount of fibroglandular breast tissue area that appears white and bright on mammograms, commonly referred to as breast percent density (PD%). MBD is a risk factor for breast cancer and a risk factor for masking tumors. However, accurate MBD estimation with visual assessment is still a challenge due to faint contrast and significant variations in background fatty tissues in mammograms. In addition, correctly interpreting mammogram images requires highly trained medical experts: it is difficult, time-consuming, expensive, and error-prone. Nevertheless, dense breast tissue can make it harder to identify breast cancer and be associated with an increased risk of breast cancer. For example, it has been reported that women with a high breast density compared to women with a low breast density have a four- to six-fold increased risk of developing the disease. The primary key of breast density computing and breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; however, most are not automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. It would be more helpful to have a computer-aided diagnosis (CAD) system to assist the doctor analyze and diagnosing it automatically. Current development in deep learning methods motivates us to improve current breast density analysis systems. The main focus of the present thesis is to develop a system for automating the breast density analysis ( such as; breast density segmentation(BDS), breast density percentage (BDP), and breast density classification ( BDC)), using deep learning techniques and applying it on the temporal mammograms after treatment for analyzing the breast density changes to find a risky and suspicious patient

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

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