Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

Parametric Procedural Models for 3D Object Retrieval, Classification and Parameterization

The amount of 3D objects has grown over the last decades, but we can expect that it will grow much further in the future. 3D objects are also becoming more and more accessible to non-expert users. The growing amount of available 3D data is welcome for everyone working with this type of data, as the creation and acquisition of many 3D objects is still costly. However, the vast majority of available 3D objects are only present as pure polygon meshes. We arguably can not assume to get meta-data and additional semantics delivered together with 3D objects stemming from non-expert or 3D scans of real objects from automatic systems. For this reason content-based retrieval and classification techniques for 3D objects has been developed. Many systems are based on the completely unsupervised case. However, previous work has shown that there are strong possibilities of highly increasing the performance of these tasks by using any type of previous knowledge. In this thesis I use procedural models as previous knowledge. Procedural models describe the construction process of a 3D object instead of explicitly describing the components of the surface. These models can include parameters into the construction process to generate variations of the resulting 3D object. Procedural representations are present in many domains, as these implicit representations are vastly superior to any explicit representation in terms of content generation, flexibility and reusability. Therefore, using a procedural representation always has the potential of outclassing other approaches in many aspects. The usage of procedural models in 3D object retrieval and classification is not highly researched as this powerful representation can be arbitrary complex to create and handle. In the 3D object domain, procedural models are mostly used for highly regularized structures like buildings and trees. However, Procedural models can deeply improve 3D object retrieval and classification, as this representation is able to offer a persistent and reusable full description of a type of object. This description can be used for queries and class definitions without any additional data. Furthermore, the initial classification can be improved further by using a procedural model: A procedural model allows to completely parameterize an unknown object and further identify characteristics of different class members. The only drawback is that the manual design and creation of specialized procedural models itself is very costly. In this thesis I concentrate on the generalization and automation of procedural models for the application in 3D object retrieval and 3D object classification. For the generalization and automation of procedural models I propose to offer different levels of interaction for a user to fulfill the possible needs of control and automation. This thesis presents new approaches for different levels of automation: the automatic generation of procedural models from a single exemplary 3D object. The semi-automatic creation of a procedural model with a sketch-based modeling tool. And the manual definition a procedural model with restricted variation space. The second important step is the insertion of parameters into the procedural model, to define the variations of the resulting 3D object. For this step I also propose several possibilities for the optimal level of control and automation: An automatic parameter detection technique. A semi-automatic deformation based insertion. And an interface for manually inserting parameters by choosing one of the offered insertion principles. It is also possible to manually insert parameters into the procedures if the user needs the full control on the lowest level. To enable the usage of procedural models directly for 3D object retrieval and classification techniques I propose descriptor-based and deep learning based approaches. Descriptors measure the difference of 3D objects. By using descriptors as comparison algorithm, we can define the distance between procedural models and other objects and order these by similarity. The procedural models are sampled and compared to retrieve an optimal object retrieval list. We can also directly use procedural models as data basis for a retraining of a convolutional neural network. By deep learning a set of procedural models we can directly classify new unknown objects without any further large learning database. Additionally, I propose a new multi-layered parameter estimation approach using three different comparison measures to parameterize an unknown object. Hence, an unknown object is not only classified with a procedural model but the approach is also able to gather new information about the characteristics of the object by using the procedural model for the parameterization of the unknown object. As a result, the combination of procedural models with the tasks of 3D object retrieval and classification lead to a meta concept of a holistically seamless system of defining, generating, comparing, identifying, retrieving, recombining, editing and reusing 3D objects

Towards parameter-less 3D mesh segmentation

This thesis focuses on the 3D mesh segmentation process. The research demonstrated how the process can be done in a parameterless approach which allows full automation with accurate results. Applications of this research include, but not limited to, 3D search engines, 3D character animation, robotics environment recognition, and augmented reality