3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods

Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method

Modelo híbrido para la segmentación de imágenes cerebrales multiespectrales de resonancia magnética

La segmentación de imágenes cerebrales, es un procedimiento necesario en aplicaciones médicas tales como, el análisis cuantitativo de la morfología de estructuras neurológicas para diagnóstico diferencial; o bien para la planeación de neurocirugías; o el estudio de la evolución temporal de un padecimiento o tratamiento específicos; los resultados de la segmentación de imágenes cerebrales también pueden coadyuvar a la generación de atlas neurológicos poblacionales. El estudio clínico de referencia es el de Resonancia Magnética, dada su capacidad para generar imágenes con una alta resolución espacial y la posibilidad de caracterizar diferentes tejidos neurológicos en un espacio multidimensional. Existen diversas propuestas para resolver el problema de la segmentación de imágenes cerebrales de Resonancia Magnética, tanto en una aproximación de segmentación por regiones como en la segmentación por contornos, sin embargo, aún se considera un problema abierto. Se han propuesto técnicas de segmentación basadas en clasificadores bayesianos o modelos de estimación no paramétrica, que en términos generales presentan problemas de definición en los bordes de las estructuras a segmentar; por otro lado se han propuesto técnicas de segmentación por contornos activos que resaltan por su desempeño en la localización del borde, sin embargo presentan problemas de convergencia. Las propuestas híbridas han arrojado mejores resultados.El mejor resultado se obtuvo para un término compuesto por una operación no lineal sobre el promedio de magnitudes de gradientes monoespectrales. Se valoraron dos tipos de acoplamiento entre la Red con Funciones de Base Radial y el modelo de contorno activo. El primero de ellos se denominó acoplamiento estático, debido a que la incorporación de la fuerza de restricción en el término de energía del contorno activo permanece sin cambio a lo largo del proceso de búsqueda del contorno final. En el acoplamiento dinámico se propone un lazo de retroalimentación, que liga la salida del contorno activo en cada iteración, con un ajuste de los parámetros de la red. Se presume que en cada iteración, la búsqueda del contorno deseado mejora, con esta información es posible actualizar los parámetros de la red que influyen en la región limitada por el contorno. Este ajuste a su vez, mejora la calidad del término de restricción que es empleado por el modelo de los contornos activos para realizar la siguiente iteración en la búsqueda del contorno óptimo. Como parte del pre-procesamiento de las imágenes, se planteó una normalización del espacio de intensidades. las variaciones en los protocolos de generación de las imágenes multiespectrales, producen imágenes cuya estadística es diferente al conjunto de aquellas empleadas para entrenar la red neuronal, sin embargo, la estructura de la distribución de las clases de tejido de interés se conserva, por lo que se empleó la transformación de Karhunen-loeve para normalizar los espacios de intensidad. Para la depuración de la estructura inicial de la red, se empleó un conjunto de imágenes reales provistas por el Hospital ABC. Para las pruebas del modelo híbrido propuesto, se emplearon imágenes cerebrales multiespectrales de RM, provistas por el simulador del Instituto Neurológico de Montréal (INM); el mismo Instituto provee el volumen etiquetado de referencia para validación de algoritmos de segmentación. Finalmente se aplicó el modelo a dos conjuntos de imágenes reales, generados bajo el protocolo ICBM y provistos también por el INM. Se realizó una comparación de los resultados obtenidos con el modelo propuesto y otros paradigmas de segmentación. De acuerdo al parámetro de evaluación del desempeño usado, el modelo propuesto obtuvo resultados muy favorables. Como resultado de la pre-segmentación con la red neuronal se obtuvo un indice de Tanimoto medio de 0.72, cabe señalar que para otros métodos de segmentación de imágenes cerebrales, este índice representa una mejora entre el 4% al 6%. El Índice de Tanimoto medio para el modelo híbrido fue de 0.74. En conclusión, se confirmó la hipótesis de trabajo referente a que la segmentación de las imágenes de RM, se ve favorecida si se considera información multiespectral en lugar de sólo considerar información monoespectral. Se confirma también la hipótesis de que, el modelo híbrido mejora los resultados que pueden obtener individualmente cada uno de los modelos acoplados.En este trabajo de investigación se propuso un modelo híbrido de segmentación de imágenes cerebrales multiespectrales de Resonancia Magnética que acopla, un modelo de clasificación supervisada, basado en una red neuronal con funciones de activación con base radial, con un modelo de contornos activos basado en una interpolación con splines cúbicos. La hibridación del modelo se planteó en dos momentos, el primero de ellos consistió en que la red neuronal hiciera la función de presegmentador y proporcionara un contorno inicial para el modelo de contornos activos; el segundo momento consistió en integrar en el término de energía del contorno activo un componente de restricción que se deriva directamente de los mapas de probabilidad posterior generados por la red; la combinación de este término de restricción con el término de energía de la imagen, coadyuvan en la convergencia del contorno final hacia el borde deseado, que en este trabajo fue el borde materia gris-materia blanca. Se probaron diferentes términos de energía derivados de la imagen, entre ellos el promedio de magnitudes de los gradientes para cada banda de intensidad, la magnitud del gradiente sobre la imagen multiespectral y el flujo de vector gradiente

Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way

IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources