Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Automatic Spine Curvature Estimation from X-ray Images of a Mouse Model

Automatic segmentation and quantification of skeletal structures has a variety of applications for biological research. Although solutions for good quality X-ray images of human skeletal structures are in existence in recent years, automatic solutions working on poor quality X-ray images of mice are rare. This paper proposes a fully automatic solution for spine segmentation and curvature quantification from X-ray images of mice. The proposed solution consists of three stages, namely preparation of the region of interest, spine segmentation, and spine curvature quantification, aiming to overcome technical difficulties in processing the X-ray images. We examined six different automatic measurements for quantifying the spine curvature through tests on a sample data set of 100 images. The experimental results show that some of the automatic measures are very close to and consistent with the best manual measurement results by annotators. The test results also demonstrate the effectiveness of the curvature quantification produced by the proposed solution in distinguishing abnormally shaped spines from the normal ones with accuracy up to 98.6%

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)The introduction of imaging techniques in 1970 is one of the most relevant historical milestones in modern medicine. Medical imaging techniques have dramatically changed our understanding of the Human anatomy and physiology. The ability to non-invasively extract visual information allowed, not only the three-dimensional representation of the internal organs and musculo-skeletal system, but also the simulation of surgical procedures, the execution of computer aided surgeries, the development of more accurate biomechanical models, the development of custom-made implants, among others. The combination of the most advanced medical imaging systems with the most advanced CAD and CAM techniques, may allow the development of custom-made implants that meet patient-speci c traits. The geometrical and functional optimization of these devices may increase implant life-expectancy, especially in patients with marked deviations from the anatomical standards. In the implant customization protocol from medical image data, there are several steps that need to be followed in a sequential way, namely: Medical Image Processing and Recovering; Accurate Image Segmentation and 3D Surface Model Generation; Geometrical Customization based on CAD and CAE techniques; FEA Optimization of the Implant Geometry; and Manufacturing using CAD-CAM Technologies. This work aims to develop the necessary procedures for custom implant development from medical image data. This includes the extraction of highly accurate three-dimensional representation of the musculo-skeletal system from the Computed Tomography imaging, and the development of customized implants, given the speci c requirements of the target anatomy, and the applicable best practices found in the literature. A two-step segmentation protocol is proposed. In the rst step the region of interest is pre-segmented in order to obtain a good approximation to the desired geometry. Next, a fully automatic segmentation re nement is applied to obtain a more accurate representation of the target domain. The re nement step is composed by several sub-steps, more precisely, the recovery of the original image, considering the limiting resolution of the imaging system; image cropping; image interpolation; and segmentation re nement over the up-sampled domain. Highly accurate segmentations of the target domain were obtained with the proposed pipeline. The limiting factor to the accurate description of the domain accuracy is the image acquisition process, rather the following image processing, segmentation and surface meshing steps. The new segmentation pipeline was used in the development of three tailor-made implants, namely, a tibial nailing system, a mandibular implant, and a Total Hip Replacement system. Implants optimization is carried with Finite Element Analysis, considering the critical loading conditions that may be applied to each implant in working conditions. The new tibial nailing system is able of sustaining critical loads without implant failure; the new mandibular endoprosthesis that allows the recovery of the natural stress and strain elds observed in intact mandibles; and the Total Hip Replacement system that showed comparable strain shielding levels as commercially available stems. In summary, in the present thesis the necessary procedures for custom implant design are investigated, and new algorithms proposed. The guidelines for the characterization of the image acquisition, image processing, image segmentation and 3D reconstruction are presented and discussed. This new image processing pipeline is applied and validated in the development of the three abovementioned customized implants, for di erent medical applications and that satisfy speci c anatomical needs.Um dos principais marcos da história moderna da medicina e a introdução da imagem médica, em meados da década de 1970. As tecnologias de imagem permitiram aumentar e potenciar o nosso conhecimento acerca da anatomia e fisiologia do corpo Humano. A capacidade de obter informação imagiológica de forma não invasiva permitiu, não são a representação tridimensional de órgãos e do sistema músculo-esquelético, mas também a simulação de procedimentos cirúrgicos, a realização de cirurgias assistidas por computador, a criação de modelos biomecânicos mais realistas, a criação de implantes personalizados, entre outros. A conjugação dos sistemas mais avançados de imagem medica com as técnicas mais avançadas de modelação e maquinagem, pode permitir o desenvolvimento de implantes personalizados mais otimizados, que vão de encontro as especificidades de cada paciente. Por sua vez, a otimização geométrica e biomecânica destes dispositivos pode permitir, quer o aumento da sua longevidade, quer o tratamento de pessoas com estruturas anatómicas que se afastam dos padrões normais. O processo de modelação de implantes a partir da imagem medica passa por um conjunto de procedimentos a adotar, sequencialmente, ate ao produto final, a saber: Processamento e Recuperação de Imagem; Segmentação de Imagem e Reconstrução tridimensional da Região de Interesse; Modelação Geométrica do Implante; Simulação Numérica para a Otimização da Geometria; a Maquinagem do Implante. Este trabalho visa o desenvolvimento dos procedimentos necessários para a criação de implantes personalizados a partir da imagem medica, englobando a extração de modelos ósseos geométricos rigorosos a partir de imagens de Tomografia Computorizada e, a partir desses modelos, desenvolver implantes personalizados baseados nas melhores praticas existentes na literatura e que satisfaçam as especificidades da anatomia do paciente. Assim, apresenta-se e discute-se um novo procedimento de segmentação em dois passos. No primeiro e feita uma pre-segmentação que visa obter uma aproximação iniciala região de interesse. De seguida, um procedimento de refinamento da segmentação totalmente automático e aplicada a segmentação inicial para obter uma descrição mais precisa do domínio de interesse. O processo de refinamento da segmentação e constituído por vários procedimentos, designadamente: recuperação da imagem original, tendo em consideração a resolução limitante do sistema de imagem; o recorte da imagem na vizinhança da região pre-segmentada; a interpolação da região de interesse; e o refinamento da segmentação aplicando a técnica de segmentação Level-Sets sobre o domínio interpolado. O procedimento de segmentação permitiu extrair modelos extremamente precisos a partir da informação imagiológica. Os resultados revelam que o fator limitante a descrição do domínio e o processo de aquisição de imagem, em detrimento dos diversos passos de processamento subsequentes. O novo protocolo de segmentação foi utilizado no desenvolvimento de três implantes personalizados, a saber: um sistema de fixação interna para a tíbia; um implante mandibular; e um sistema para a Reconstrução Total da articulação da Anca. A otimização do comportamento mecânico dos implantes foi feita utilizado o Método dos Elementos Finitos, tendo em conta os carregamentos críticos a que estes podem estar sujeitos durante a sua vida útil. O sistema de fixação interna para a tíbia e capaz de suportar os carregamentos críticos, sem que a sua integridade mecânica seja comprometida; o implante mandibular permite recuperar os campos de tensão e deformação observados em mandíbulas intactas; e a Prótese Total da Anca apresenta níveis de strain shielding ao longo do fémur proximal comparáveis com os níveis observados em dispositivos comercialmente disponíveis. Em suma, nesta tese de Doutoramento são investigados e propostos novos procedimentos para o projeto de implantes feitos por medida. São apresentadas e discutidas as linhas orientadoras para a caracterização precisa do sistema de aquisição de imagem, para o processamento de imagem, para a segmentação, e para a reconstrução 3D das estruturas anatómicas a partir da imagem medica. Este conjunto de linhas orientadoras é aplicado e validado no desenvolvimento de três implantes personalizados, citados anteriormente, para aplicações médicas distintas e que satisfazem as necessidades anatómicas específicas de cada paciente.Fundação para a Ciência e Tecnologia (FCT

Image analysis-based framework for adaptive and focal radiotherapy

It is estimated that more than 60% of cancer patients will receive radiotherapy (RT). Medical images acquired from different imaging modalities are used to guide the entire RT process from the initial treatment plan to fractionated radiation delivery. Accurate identification of the gross tumor volume (GTV) on computed tomography (CT), acquired at different time points, is crucial for the success of RT. In addition, complementary information from magnetic resonance imaging (MRI), positron emission tomography (PET), cone-beam computed tomography (CBCT) and electronic portal imaging device (EPID) is often used to obtain better definition of the target, track disease progression and update the radiotherapy plan. However, identifying tumor volumes on medical image data requires significant clinical experience and is extremely time consuming. Computer-based methods have the potential to assist with this task and improve radiotherapy. In this thesis a method was developed for automatically identifying the tumor volume on medical images. The method consists of three main parts: (1) a novel rigid image registration method based on scale invariant feature transform (SIFT) and mutual information (MI); (2) a non-rigid registration (deformable registration) method based on the cubic B-spline and a novel similarity function; (3) a gradient-based level set method that used the registered information as prior knowledge for further segmentation to detect changes in the patient from disease progression or regression and to account for the time difference between image acquisition. Validation was carried out by a clinician and by using objective methods that measure the similarity between the anatomy defined by a clinician and by the method proposed. With this automatic approach it was possible to identify the tumor volume on different images acquired at different time points in the radiotherapy workflow. Specifically, for lung cancer a mean error of 3.9% was found; clinically acceptable results were found for 12 of the 14 prostate cancer cases; and a similarity of 84.44% was achieved for the nasal cancer data. This framework has the potential ability to track the shape variation of tumor volumes over time, and in response to radiotherapy, and could therefore, with more validation, be used for adaptive radiotherapy