Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

An image segmentation and registration approach to cardiac function analysis using MRI

Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent years, significant progress has been made in the care and treatment of patients with such diseases. A crucial factor for this progress has been the development of magnetic resonance (MR) imaging which makes it possible to diagnose and assess the cardiovascular function of the patient. The ability to obtain high-resolution, cine volume images easily and safely has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during the image acquisition process. With the development of advanced MR imaging acquisition technologies, 3D MR imaging is more and more clinically feasible. This recent development has allowed new potentially 3D image analysis technologies to be deployed. However, quantitative analysis of cardiovascular system from the images remains a challenging topic. The work presented in this thesis describes the development of segmentation and motion analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic resonance (CMR) images. The first main contribution of the thesis is the development of a fully automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art techniques. The proposed segmentation technique is capable of generating an accurate 3D segmentation from multiple image sequences. The proposed segmentation technique is robust even in the presence of pathological changes, large anatomical shape variations and locally varying contrast in the images. Another main contribution of this thesis is the development of motion tracking techniques that can integrate motion information from different sources. For example, the radial motion of the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of both longitudinal and circumferential motion. We propose a novel technique based on non-rigid image registration for the myocardial motion estimation using both untagged and 3D tagged MR images. The novel aspect of our technique is its simultaneous use of complementary information from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted to maximise the utility of information from both images. The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard FFD approach, a significant increase in registration accuracy can be observed in datasets with discontinuous motion patterns. Both the segmentation and motion tracking techniques presented in this thesis have been applied to clinical studies. We focus on two important clinical applications that can be addressed by the techniques proposed in this thesis. The first clinical application aims at measuring longitudinal changes in cardiac morphology and function during the cardiac remodelling process. The second clinical application aims at selecting patients that positively respond to cardiac resynchronization therapy (CRT). The final chapter of this thesis summarises the main conclusions that can be drawn from the work presented here and also discusses possible avenues for future research

Shape Analysis Based Strategies for Evaluation of Adaptations in In Vivo Right Ventricular Geometry and Mechanics as Effected by Pulmonary Hypertension

Pulmonary hypertension (PH) is a deadly disease, which as it progresses over time alters many aspects of the afflicted heart, and particularly the right ventricle (RV), such as its size, shape, and mechanical material properties. However, due to the limitations of what can be measured noninvasively in a standard clinical setting and the difficulty caused by the intrinsic complexity of the human RV, there has been little success to-date to identify clinically obtainable metrics of RV shape, deformation, or material properties that are quantitatively linked to the onset and progression of PH. Towards addressing this challenge, this work proposes the use of the shape and shape change of the RV, which is measurable from standard clinical imaging, along with statistical analysis and inverse material characterization strategies to identify new metrics of RV mechanical function that will be uniquely predictive of the state of the heart subject to PH. Thus, this thesis can be broken into two components: the first is statistical shape analysis of the RV, and the second is inverse characterization of heart wall mechanical material properties from RV shape change and measurable hemodynamics. For the statistical shape analysis investigation, a custom approach using harmonic mapping and proper orthogonal decomposition is applied to determine the fundamental components of shape (i.e., modes) from a dataset of 50 patients with varying states of PH, including some without PH at all. For the inverse characterization work, a novel method was developed to estimate the heterogeneous properties of a structure, given only the target shape of that structure, after a known excitation is applied to deform the structure. Lastly, the inverse characterization algorithm was extended to be applicable to actual in vivo cardiac data, particularly through the inclusion of a registration step to account for the organ-scale rotation and translation of the heart. Future work remains to expand on the computational efficiency of this inverse solution estimation procedure, and to further evaluate and improve upon the consistency and clinical interpretability of the material property estimates

Quantification of MRI-derived myocardial motion in specified cardiac disorders

Several cardiac diseases affect myocardial function, with local myocardial deformation receiving much attention over the past few years. This work aimed to examine whether globally and locally analyzed quantitative cardiovascular magnetic resonance imaging-derived strain, rotation, and torsion of the heart would bring additional value and deeper understanding to myocardial mechanics in specified cardiovascular disorders. Patients with rheumatoid arthritis, tetralogy of Fallot, hereditary gelsolin amyloidosis, and hypertrophic cardiomyopathy, together with healthy controls, were investigated. A non-rigid registration-based software solution for myocardial tagging and feature tracking analysis was used for the quantification of left ventricular and right ventricular global and regional strain in different directions. Quantitative motion analysis showed that early treatment of rheumatoid arthritis was useful in retaining the diastolic function of the left ventricle. In adolescents with tetralogy of Fallot, right ventricular circumferential strain was increased relative to healthy controls. Tetralogy of Fallot subjects with increased pulmonary regurgitation had higher right ventricular longitudinal strain than subjects with less pulmonary regurgitation; this has been considered a compensation mechanism. Hereditary gelsolin amyloidosis showed local myocardial changes focused on the basal plane of the left ventricle and differing from the more common light-chain cardiac amyloidosis. The non-rigid registration-based technique was compared with the harmonic phase-based method with Gabor filtering in the analysis of myocardial tagging-derived rotation and torsion in subjects with hypertrophic cardiomyopathy. The absolute values obtained with the two software methods were significantly different, however, neither software showed significant differences in patients with hypertrophic cardiomyopathy relative to healthy controls. Motion parameters of both ventricles were associated with other quantitative cardiac magnetic resonance imaging parameters, such as volumetric measurements and T1 relaxation times, in the studies of this thesis. Tagging and feature tracking-derived motion parameters showed significant findings in local myocardial motion in rheumatoid arthritis, tetralogy of Fallot,and hereditary gelsolin amyloidosis. Software-based reference values are required when comparing motion parameters between study subjects. Currently, no standardization for measuring different deformation parameters, such as strain, rotation, or torsion exists, and several software solutions are available for analyzing these parameters. Variability between different software solutions and individual observers should be recognized.Useat sydänsairaudet vaikuttavat sydänlihaksen paikalliseen liikkeeseen, jonka vuoksi sydänlihaksen liikkeen tutkiminen on herättänyt paljon mielenkiintoa muutaman viime vuoden aikana. Tämän työn tavoitteena oli tutkia magneettikuvauksessa määritettyjä sydänlihaksen globaaleja ja paikallisia kvantitatiivisia liikeparametrejä eri sydänsairauksissa. Väitöskirjan osatöissä tutkittiin nivelreumapotilaita, Fallotin tetralogia -potilaita, Meretojantautipotilaita ja hypertrofisen kardiomyopatian omaavia potilaita, yhdessä terveiden verrokkien kanssa. Elastisen kuvarekisteröinnin omaavaa ohelmistoratkaisua käytettiin sydänlihaksen kvantitatiivisen venymän mittaamiseen eri suunnissa sydämen vasenta ja oikeaa kammiota. Kvantitatiivinen liikeanalyysi osoitti, että varhaisen nivelreuman lääkehoito kannattaa, jotta sydämen vasemman kammion diastolinen funktio saadaan ylläpidettyä. Teini-ikäisillä Fallotin tetralogia -potilailla oikean kammion kehän suuntainen venymä oli selvästi voimakkaampaa kuin terveillä verrokeilla. Lisäksi Fallotin tetralogia -potilailla, joilla oli suuri pulmonaaliläpän vuoto, oli voimakkaampi oikean kammion pitkittäissuuntainen venymä, kuin potilailla, joilla vuoto oli pienempää; tämän ajateltiin olevan sydänlihaksen kompensaatiomekanismi. Meretojantautipotilailla havaittiin paikallisia sydänlihaksen liikkeen ja kudoskoostumuksen muutoksia erityisesti sydämen vasemman kammion basaalitasossa. Elastisen kuvarekisteröinnin menetelmää verrattiin harmoniseen Gabor -suodatettuun menetelmään sydänlihaksen vasemman kammion kiertymän ja väännön analysoinnissa. Näillä kahdella menetelmällä määritetyt absoluuttiset kiertymän ja väännön arvot erosivat merkittävästi toisistaan, mutta kummallakaan menetelmällä määritetyt kiertymän ja väännön arvot eivät eronneet merkittävästi hypertrofisen kardiomyopatian omaavien potilaiden ja terveiden verrokkien välillä. Sydämen vasemman ja oikean kammion liikeparametrejä verrattiin muihin kvantitatiivisiin sydämen magneettikuvauksen parametreihin, kuten kammioiden volumetrisiin mittauksiin ja sydänlihaksen T1 relaksaatioaikoihin, väitöskirjan eri osatöissä. Sydämen magneettikuvauksessa määritetyt kvantitatiivisen liikeanalyysin eri parametrit osoittivat merkittäviä löydöksiä sydänlihaksen toiminnassa nivelreumassa, Fallotin tetralogiassa ja Meretojantaudissa. Ohjelmistokohtaiset referenssiarvot ovat tarpeen, kun absoluuttisia liikeparametriarvoja vertaillaan eri yksilöiden välillä. Tällä hetkellä ei ole olemassa standardeja eri liikekomponenttien mittaamiselle ja eri ohjelmistoratkaisuja on useita erilaisia. Vaihteluväli erilaisia ohjelmistoratkaisuja käytettäessä, ja eri tarkkailijoiden välinen vaihtelu, on syytä tiedostaa

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool