Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Ultrasonic differentiation of healthy and cancerous neural tissue

It is well documented that intraoperative ultrasound offers improvements to the extent of tumour resected in neurosurgery but currently fails to depict the boundaries of more invasive tumours. Quantitative ultrasound (QUS) is a technique that models ultrasound scattering in tissue mathematically. It can act as a quantitative tool to identify cancerous regions and be used to define features which can train a machine learning (ML) classifier. The use of QUS to differentiate healthy and malignant brain tissue is the objective of this thesis. This work began with a proof of concept study which saw the effective implementation of QUS with a linear array transducer, at conventional frequencies, on phantom materials. The results were then used to train a K-nearest neighbours (KNN) binary classifier to differentiate between two soft tissues. Insight into the most practical parameters for near real time tissue identification was achieved, as well as the opportunity to produce parametric images for various QUS parameters. The effects of freezing and fixation of tissue on QUS results were also considered. The experimental design was developed to obtain a higher lateral spatial resolution before applying it to ex vivo human samples of ten healthy and eight high-grade glioma (HGG) tissues. This was accomplished with both a linear array and a single element scanning system, at centre frequencies of 25 and 74 MHz, respectively. The SoS and attenuation were found to be higher, on average, in the tumour samples than in the healthy tissue. The homodyned K-distribution (HK) parameters alone could distinguish between healthy and HGG tissue to 96% accuracy at 74 MHz, suggesting this is a viable solution for residual HGG detection. To explore the potential of ML with a larger data set, and to extend the study to low grade glioma (LGG) tissue, acoustic impedance maps based on 300 previously recorded microscope histology images of each tissue type were created. The interaction with high frequency (HF) ultrasound was explored using finite element analysis and QUS parameters were obtained. A classification algorithm was able to differentiate healthy and HGG to near perfect accuracy, but a significantly lower accuracy of 79% was found when distinguishing LGG from healthy tissue maps. This research represents a step forward in the otherwise unexplored landscape of HF QUS in brain tissue which necessitates further work to transition from laboratory based experiments to in vivo QUS to aid intraoperative glioma detection

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Image processing in medicine advances for phenotype characterization, computer-assisted diagnosis and surgical planning

En esta Tesis presentamos nuestras contribuciones al estado del arte en procesamiento digital de imágenes médicas, articulando nuestra exposición en torno a los tres principales objetivos de la adquisición de imágenes en medicina: la prevención, el diagnóstico y el tratamiento de las enfermedades. La prevención de la enfermedad se puede conseguir a veces mediante una caracterización cuidadosa de los fenotipos propios de la misma. Tal caracterización a menudo se alcanza a partir de imágenes. Presentamos nuestro trabajo en caracterización del enfisema pulmonar a partir de imágenes TAC (Tomografía Axial Computerizada) de tórax en alta resolución, a través del análisis de las texturas locales de la imagen. Nos proponemos llenar el vacío existente entre la práctica clínica actual, y las sofisticadas pero costosas técnicas de caracterización de regiones texturadas, disponibles en la literatura. Lo hacemos utilizando la distribución local de intensidades como un descriptor adecuado para determinar el grado de destrucción de tejido en pulmones enfisematosos. Se presentan interesantes resultados derivados del análisis de varios cientos de imágenes para niveles variables de severidad de la enfermedad, sugiriendo tanto la validez de nuestras hipótesis, como la pertinencia de este tipo de análisis para la comprensión de la enfermedad pulmonar obstructiva crónica. El procesado de imágenes médicas también puede asistir en el diagnóstico y detección de enfermedades. Presentamos nuestras contribuciones a este campo, que consisten en técnicas de segmentación y cuantificación de imágenes dermatoscópicas de lesiones de la piel. La segmentación se obtiene mediante un novedoso algoritmo basado en contornos activos que explota al máximo el contenido cromático de las imágenes, gracias a la maximización de la discrepancia mediante comparaciones cross-bin. La cuantificación de texturas en lesiones melanocíticas se lleva a cabo utilizando un modelado de los patrones de pigmentación basado en campos aleatorios de Markov, en un esfuerzo por adoptar la tendencia emergente en dermatología: la detección de la malignidad mediante el análisis de la irregularidad de la textura. Los resultados para ambas técnicas son validados con un conjunto significativo de imágenes dermatológicas, sugiriendo líneas interesantes para la detección automática del melanoma maligno. Cuando la enfermedad ya está presente, el tratamiento digital de imágenes puede asistir en la planificación quirúrgica y la intervención guiada por imagen. La planificación terapeútica, ejemplicada por la planificación de cirugía plástica usando realidad virtual, se aborda en nuestro trabajo en segmentación de hueso/grasa/músculo en imágenes TAC. Usando un abordaje interactivo e incremental, nuestro sistema permite obtener segmentaciones precisas a partir de unos cuantos clics de ratón para una gran variedad de condiciones de adquisición y frente a anatomícas anormales. Presentamos nuestra metodología, y nuestra validación experimental profusa basada tanto en segmentaciones manuales como en valoraciones subjetivas de los usuarios, e indicamos referencias al lector que detallan los beneficios obtenidos con el uso de la plataforma de planifificación que utiliza nuestro algoritmo. Como conclusión presentamos una disertación final sobre la importancia de nuestros resultados y las líneas probables de trabajo futuro hacía el objetivo último de mejorar el cuidado de la salud mediante técnicas de tratamiento digital de imágenes médicas.In this Thesis we present our contributions to the state-of-the-art in medical image processing, articulating our exposition around the three main roles of medical imaging: disease prevention, diagnosis and treatment. Disease prevention can sometimes be achieved by proper characterization of disease phenotypes. Such characterization is often attained from the standpoint of imaging. We present our work in characterization of emphysema from highresolution computed-tomography images via quanti_cation of local texture. We propose to _ll the gap between current clinical practice and sophisticated texture approaches by the use of local intensity distributions as an adequate descriptor for the degree of tissue destruction in the emphysematous lung. Interesting results are presented from the analysis of several hundred datasets of lung CT for varying disease severity, suggesting both the correctness of our hypotheses and the pertinence of _ne emphysema quanti_cation for understanding of chronic obstructive pulmonary disease. Medical image processing can also assist in the diagnosis and detection of disease. We introduce our contributions to this_eld, consisting of segmentation and quanti_cation techniques in application to dermatoscopy images of skin lesions. Segmentation is achieved via a novel active contour algorithm that fully exploits the color content of the images, via cross-bin histogram dissimilarity maximization. Texture quanti_cation in the context of melanocytic lesions is performed using modelization of the pigmentation patterns via Markov random elds, in an e_ort to embrace the emerging trend in dermatology: malignancy assessment based on texture irregularity analysis. Experimental results for both, the segmentation and quanti_cation proposed techniques, will be validated on a signi_cant set of dermatoscopy images, suggesting interesting pathways towards automatic detection and diagnosis of malignant melanoma. Once disease has occurred, image processing can assist in therapeutical planning and image-guided intervention. Therapeutical planning, exempli_ed by virtual reality surgical planning, is tackled by our work in segmentation of bone/fat/muscle in CT images for plastic surgery planning. Using an interactive, incremental approach, our system is able to provide accurate segmentations based on a couple of mouse-clicks for a wide variety of imaging conditions and abnormal anatomies. We present our methodology, and provide profuse experimental validation based on manual segmentations and subjective assessment, and refer the reader to related work reporting on the clinical bene_ts obtained using the virtual reality platform hosting our algorithm. As a conclusion we present a _nal dissertation on the signi_cance of our results and the probable lines of future work towards fully bene_tting healthcare using medical image processing

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population