Modelos Deformáveis em Imagem Médica

Modelos deformáveis são actualmente bastante utilizados em imagem médica pois, através da utilização de princípios físicos, simulam de forma bastante satisfatória o comportamento dos objectos reais.Basicamente os modelos deformáveis são inicializados junto dos objectos a considerar, por processos automáticos ou semi-automáticos, e a aproximação para a posição final desejada é conseguida através de um processo de minimização de energia. Esta minimização de energia é verificada quando o modelo atinge o equilíbrio, entre as suas forças internas e as forças externas originadas pelos dados e por eventuais forças impostas pelo utilizador.Neste relatório são apresentados os fundamentos dos modelos deformáveis e indicados alguns exemplos de aplicação em imagem médica, nomeadamente na segmentação, no emparelhamento, no alinhamento e na reconstrução de dados 2D e 3D.Palavras-chave: Contornos activos, imagem médica, modelos deformáveis.Deformable models are currently very used in medical image since, through the use of physical principles, they simulate quite satisfactory the real objects behavior.Basically the deformable models are placed in the image near to the objects to be considered, by automatic or semi-automatic processes, and the approach to the desired final position is obtained through an energy minimization process. This energy minimization is verified when the model reaches the equilibrium, between its internal forces and the external forces originated by the data and eventual forces imposed by the user.In this report are presented the deformable models fundaments and indicated some application examples in medical imaging field, namely in segmentation, matching, alignment and in the reconstruction of 2D and 3D data.Keywords: Active contours, deformable models, medical image

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Deep Networks Based Energy Models for Object Recognition from Multimodality Images

Object recognition has been extensively investigated in computer vision area, since it is a fundamental and essential technique in many important applications, such as robotics, auto-driving, automated manufacturing, and security surveillance. According to the selection criteria, object recognition mechanisms can be broadly categorized into object proposal and classification, eye fixation prediction and saliency object detection. Object proposal tends to capture all potential objects from natural images, and then classify them into predefined groups for image description and interpretation. For a given natural image, human perception is normally attracted to the most visually important regions/objects. Therefore, eye fixation prediction attempts to localize some interesting points or small regions according to human visual system (HVS). Based on these interesting points and small regions, saliency object detection algorithms propagate the important extracted information to achieve a refined segmentation of the whole salient objects. In addition to natural images, object recognition also plays a critical role in clinical practice. The informative insights of anatomy and function of human body obtained from multimodality biomedical images such as magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), computed tomography (CT) and positron emission tomography (PET) facilitate the precision medicine. Automated object recognition from biomedical images empowers the non-invasive diagnosis and treatments via automated tissue segmentation, tumor detection and cancer staging. The conventional recognition methods normally utilize handcrafted features (such as oriented gradients, curvature, Haar features, Haralick texture features, Laws energy features, etc.) depending on the image modalities and object characteristics. It is challenging to have a general model for object recognition. Superior to handcrafted features, deep neural networks (DNN) can extract self-adaptive features corresponding with specific task, hence can be employed for general object recognition models. These DNN-features are adjusted semantically and cognitively by over tens of millions parameters corresponding to the mechanism of human brain, therefore leads to more accurate and robust results. Motivated by it, in this thesis, we proposed DNN-based energy models to recognize object on multimodality images. For the aim of object recognition, the major contributions of this thesis can be summarized below: 1. We firstly proposed a new comprehensive autoencoder model to recognize the position and shape of prostate from magnetic resonance images. Different from the most autoencoder-based methods, we focused on positive samples to train the model in which the extracted features all come from prostate. After that, an image energy minimization scheme was applied to further improve the recognition accuracy. The proposed model was compared with three classic classifiers (i.e. support vector machine with radial basis function kernel, random forest, and naive Bayes), and demonstrated significant superiority for prostate recognition on magnetic resonance images. We further extended the proposed autoencoder model for saliency object detection on natural images, and the experimental validation proved the accurate and robust saliency object detection results of our model. 2. A general multi-contexts combined deep neural networks (MCDN) model was then proposed for object recognition from natural images and biomedical images. Under one uniform framework, our model was performed in multi-scale manner. Our model was applied for saliency object detection from natural images as well as prostate recognition from magnetic resonance images. Our experimental validation demonstrated that the proposed model was competitive to current state-of-the-art methods. 3. We designed a novel saliency image energy to finely segment salient objects on basis of our MCDN model. The region priors were taken into account in the energy function to avoid trivial errors. Our method outperformed state-of-the-art algorithms on five benchmarking datasets. In the experiments, we also demonstrated that our proposed saliency image energy can boost the results of other conventional saliency detection methods

What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results