Automatic Prostate Segmentation in Ultrasound Images using Gradient Vector Flow Active Contour

ABSTRACT: Prostate cancer is one of the leading causes of death by cancer among men in the world. Ultrasonography is said to be the safest technique in medical imaging so it is used extensively in prostate cancer detection. On the other hand, determining of prostate's boundary in TRUS (Transrectal Ultrasound) images is very necessary in lots of treatment methods prostate cancer. So the first and essential step for computer aided diagnosis (CAD) is the automatic prostate segmentation that is an open problem yet. But the low SNR, presence of strong speckle noise, Weak edges and shadow artifacts in these kinds of images limit the effectiveness of classical segmentation schemes. The classical segmentation methods fail completely or require post processing step to remove invalid object boundaries in the segmentation results. This paper has proposed a fully automatic algorithm for prostate segmentation in TRUS images that overcomes the explained problems completely. The presented algorithm contains three main stages. First, morphological smoothing and stick's filter are used for noise removing. A neural network is employed in the second step to find a point in prostate region. Finally in the last step, the prostate boundaries are extracted by GVF active contour. Some experiments for the performance validity of the presented method, compared with the extracted prostate by the proposed algorithm with manually-delineated boundaries by radiologist. The results show that our method extracts prostate boundaries with mean square area error lower than 4.4%

Anniversary Paper: Evolution of ultrasound physics and the role of medical physicists and the AAPM and its journal in that evolution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134810/1/mp2048.pd

A novel NMF-based DWI CAD framework for prostate cancer.

In this thesis, a computer aided diagnostic (CAD) framework for detecting prostate cancer in DWI data is proposed. The proposed CAD method consists of two frameworks that use nonnegative matrix factorization (NMF) to learn meaningful features from sets of high-dimensional data. The first technique, is a three dimensional (3D) level-set DWI prostate segmentation algorithm guided by a novel probabilistic speed function. This speed function is driven by the features learned by NMF from 3D appearance, shape, and spatial data. The second technique, is a probabilistic classifier that seeks to label a prostate segmented from DWI data as either alignat, contain cancer, or benign, containing no cancer. This approach uses a NMF-based feature fusion to create a feature space where data classes are clustered. In addition, using DWI data acquired at a wide range of b-values (i.e. magnetic field strengths) is investigated. Experimental analysis indicates that for both of these frameworks, using NMF producing more accurate segmentation and classification results, respectively, and that combining the information from DWI data at several b-values can assist in detecting prostate cancer

Shape Deformation Statistics and Regional Texture-Based Appearance Models for Segmentation

Transferring identified regions of interest (ROIs) from planning-time MRI images to the trans-rectal ultrasound (TRUS) images used to guide prostate biopsy is difficult because of the large difference in appearance between the two modalities as well as the deformation of the prostate's shape caused by the TRUS transducer. This dissertation describes methods for addressing these difficulties by both estimating a patient's prostate shape after the transducer is applied and then locating it in the TRUS image using skeletal models (s-reps) of prostate shapes. First, I introduce a geometrically-based method for interpolating discretely sampled s-reps into continuous objects. This interpolation is important for many tasks involving s-reps, including fitting them to new objects as well as the later applications described in this dissertation. This method is shown to be accurate for ellipsoids where an analytical solution is known. Next, I create a method for estimating a probability distribution on the difference between two shapes. Because s-reps live in a high-dimensional curved space, I use Principal Nested Spheres (PNS) to transform these representations to instead live in a flat space where standard techniques can be applied. This method is shown effective both on synthetic data as well as for modeling the deformation caused by the TRUS transducer to the prostate. In cases where appearance is described via a large number of parameters, such as intensity combined with multiple texture features, it is computationally beneficial to be able to turn these large tuples of descriptors into a scalar value. Using the inherent localization properties of s-reps, I develop a method for using regionally-trained classifiers to turn appearance tuples into the probability that the appearance tuple in question came from inside the prostate boundary. This method is shown to be able to accurately discern inside appearances from outside appearances over a large majority of the prostate boundary. Finally, I combine these techniques into a deformable model-based segmentation framework to segment the prostate in TRUS. By applying the learned mean deformation to a patient's prostate and then deforming it so that voxels with high probability of coming from the prostate's interior are also in the model's interior, I am able to generate prostate segmentations which are comparable to state of the art methods.Doctor of Philosoph

Registration of magnetic resonance and ultrasound images for guiding prostate cancer interventions

Prostate cancer is a major international health problem with a large and rising incidence in many parts of the world. Transrectal ultrasound (TRUS) imaging is used routinely to guide surgical procedures, such as needle biopsy and a number of minimally-invasive therapies, but its limited ability to visualise prostate cancer is widely recognised. Magnetic resonance (MR) imaging techniques, on the other hand, have recently been developed that can provide clinically useful diagnostic information. Registration (or alignment) of MR and TRUS images during TRUS-guided surgical interventions potentially provides a cost-effective approach to augment TRUS images with clinically useful, MR-derived information (for example, tumour location, shape and size). This thesis describes a deformable image registration framework that enables automatic and/or semi-automatic alignment of MR and 3D TRUS images of the prostate gland. The method combines two technical developments in the field: First, a method for constructing patient-specific statistical shape models of prostate motion/deformation, based on learning from finite element simulations of gland motion using geometric data from a preoperative MR image, is proposed. Second, a novel “model-to-image” registration framework is developed to register this statistical shape model automatically to an intraoperative TRUS image. This registration approach is implemented using a novel model-to-image vector alignment (MIVA) algorithm, which maximises the likelihood of a particular instance of a statistical shape model given a voxel-intensity-based feature vector that represents an estimate of the surface normal vectors at the boundary of the organ in question. Using real patient data, the MR-TRUS registration accuracy of the new algorithm is validated using intra-prostatic anatomical landmarks. A rigorous and extensive validation analysis is also provided for assessing the image registration experiments. The final target registration error after performing 100 MR–TRUS registrations for each patient have a median of 2.40 mm, meaning that over 93% registrations may successfully hit the target representing a clinically significant lesion. The implemented registration algorithms took less than 30 seconds and 2 minutes for manually defined point- and normal vector features, respectively. The thesis concludes with a summary of potential applications and future research directions

Spherical Harmonics on constitutive equations for biological cells

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2019.Desenvolvem-se e avaliam-se neste trabalho modelos constitutivos não-lineares incluindo o estudo de grandes deformações com o objetivo de modelar células biológicas representadas por elementos de cascas finas. É utilizada como ponto de partida a formulação clássica de elementos de cascas finas, considerando as hipóteses de Kirchhoff que apresentam como mais importante característica a redução dimensional. Esta é atingida derivando tensões 2D como médias das tensões 3D pela integração direta sob a espessura da casca. Para a definição da deformação do continuo é utilizada uma descrição Lagrangiana. As células biológicas não podem ser modeladas de forma correta utilizando modelos constitutivos lineares. Especificamente no estudo dos glóbulos vermelhos devem ser considerados: o comportamento elástico não linear e o aporte da viscosidade da parede da célula. Consequentemente, neste trabalho, modelos hiperelasticos são implementados junto ao modelo de Kelvin-Voigth para obter um modelo viscoelástico. Na implementação computacional Funções de Esféricos Harmônicos são utilizadas para sintetizar as principais variáveis, esforços e deslocamentos. Isto se deve a que a geometria dos glóbulos vermelhos pode ser descrita de forma simples utilizando coordenadas esféricas. Resultando numa implementação de baixo custo computacional que consegue lidar com altas não linearidades. Este trabalho apresenta uma formulação de um método indireto pois consiste no cálculo de coeficientes da expansão de Esféricos Harmônicos, sendo que estes coeficientes não têm sentido físico. É importante mencionar que o projeto se encontra num estágio inicial e não foi encontrado na literatura uma aplicação utilizando teoria de cascas, Harmônicos Esféricos junto com modelos constitutivos lidando com grandes deformações. Finalmente o método é validado e estudado suas possíveis aplicações.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) e Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).In this work, constitutive models are developed and evaluated with the aim of modeling biological cells represented by thin shell elements in a second-order analysis. The classical formulation of thin shell elements is used while considering dimensional reduction, which is the main feature of the Kirchhoff hypotheses. This reduction is achieved by deriving two-dimensional stresses as averages of the true three-dimensional stresses by means of direct integration through the shell thickness. A Lagrangian description is used to define the deformation of the continuum. Biological cells cannot be correctly modeled using linear constitutive relations. Specifically, in the study of red blood cells, one should consider both their nonlinear elastic behavior and the contribution of the cell wall viscosity. Consequently, hyperelastic constitutive equations are implemented using the Kelvin-Voigt approach to obtain a viscoelastic model. In the computational implementation, spherical harmonic functions are used to synthesize the main variables, resultant forces and displacements since the geometry of red blood cells can be simply described using spherical coordinates. As a result, a low-cost computational implementation for highly nonlinear analyses is obtained. This work presents a formulation of an indirect method since consists on the calculation of the expansion coefficients of a Spherical Harmonic Analysis, these coefficients have no physical meaning. It is important to mention that this work is part of a project that is at an early stage. In the literature no application was found using shell theory, Spherical Harmonics with constitutive models dealing with large deformations. Finally, the method is validated and its possible applications are discussed

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

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

MRI-based radiomics: Quantifying the stability and reproducibility of tumour heterogeneity in vivo and in a 3D printed phantom

Magnetic resonance imaging (MRI) is a key component in the oncology workflow. Radiomics analysis is a new approach that uses standard of care (SOC) magnetic resonance (MR) images to non-invasively characterise tumour heterogeneity. For radiomics to be reliable, the imaging features measured must be stable and reproducible. This thesis aims to quantify the stability and reproducibility of MRI-based radiomics in vivo and in a 3D printed phantom. Chapter 4 explores the feasibility of constructing a 3D printed phantom using an MRI visible material (‘red resin’). The study shows that the material used to construct an anthropomorphic skull phantom mimicked human cortical bone with a T2* of 411 ± 19 µs. The phantom material provided sufficient signal for tissue segmentation however was only visible with an ultrashort echo time sequence, not commonly used in SOC imaging. Chapter 5 investigates a high temperature resin (‘white resin’) where a texture object was developed for analysis. The ‘white resin’ was visible using SOC sequences. The interscanner repeatability measurements of the texture phantom demonstrated high reproducibility with 76% of texture features having an ICC > 0.9. In chapter 6, further texture and shape objects were developed and employed in a multi-centre study assessing inter and intrascanner variation of MRI-based radiomics. The phantom was stable over a period of 12 months, with a T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. The study also found that histogram features were more stable (ICC > 0.8 for 67%) compared to texture (ICC > 0.8 for 58%) and shape texture (ICC > 0.8 for 0%) across the 8 scanners. In chapter 7, phantom measurements found that radiomics features were more sensitive to changes of image resolution and noise. The in vivo test-retest component of chapter 7 detected many unstable features not suitable for use in a radiomics prognostic model. In chapter 8, of the 83 features computed only 19 features had significant changes between the baseline, mid and post radiation treatment and may be informative to assess rectal cancer treatment response. When considering using radiomics analysis for SOC MRI scans, caution must be taken to ensure imaging protocols, imaging equipment including scanners and coils are consistent to improve intra and inter-institutional feature robustness. This can be achieved with regular quality assurance (QA) of imaging protocols using a suitable phantom and appropriate feature selection using phantom and in vivo datasets