Machine Learning Methods for Medical and Biological Image Computing

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries in neuroscience. In this thesis, I propose computational methods using high-level features for automated analysis of brain images at different levels. At the brain function level, I develop a deep learning based framework for completing and integrating multi-modality neuroimaging data, which increases the diagnosis accuracy for Alzheimer’s disease. At the cellular level, I propose to use three dimensional convolutional neural networks (CNNs) for segmenting the volumetric neuronal images, which improves the performance of digital reconstruction of neuron structures. I design a novel CNN architecture such that the model training and testing image prediction can be implemented in an end-to-end manner. At the molecular level, I build a voxel CNN classifier to capture discriminative features of the input along three spatial dimensions, which facilitate the identification of secondary structures of proteins from electron microscopy im-ages. In order to classify genes specifically expressed in different brain cell-type, I propose to use invariant image feature descriptors to capture local gene expression information from cellular-resolution in situ hybridization images. I build image-level representations by applying regularized learning and vector quantization on generated image descriptors. The developed computational methods in this dissertation are evaluated using images from medical and biological experiments in comparison with baseline methods. Experimental results demonstrate that the developed representations, formulations, and algorithms are effective and efficient in learning from brain imaging data

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Doctor of Philosophy

dissertationMagnetic Resonance (MR) is a relatively risk-free and flexible imaging modality that is widely used for studying the brain. Biophysical and chemical properties of brain tissue are captured by intensity measurements in T1W (T1-Weighted) and T2W (T2-Weighted) MR scans. Rapid maturational processes taking place in the infant brain manifest as changes in co{\tiny }ntrast between white matter and gray matter tissue classes in these scans. However, studies based on MR image appearance face severe limitations due to the uncalibrated nature of MR intensity and its variability with respect to changing conditions of scan. In this work, we develop a method for studying the intensity variations between brain white matter and gray matter that are observed during infant brain development. This method is referred to by the acronym WIVID (White-gray Intensity Variation in Infant Development). WIVID is computed by measuring the Hellinger Distance of separation between intensity distributions of WM (White Matter) and GM (Gray Matter) tissue classes. The WIVID measure is shown to be relatively stable to interscan variations compared with raw signal intensity and does not require intensity normalization. In addition to quantification of tissue appearance changes using the WIVID measure, we test and implement a statistical framework for modeling temporal changes in this measure. WIVID contrast values are extracted from MR scans belonging to large-scale, longitudinal, infant brain imaging studies and modeled using the NLME (Nonlinear Mixed Effects) method. This framework generates a normative model of WIVID contrast changes with time, which captures brain appearance changes during neurodevelopment. Parameters from the estimated trajectories of WIVID contrast change are analyzed across brain lobes and image modalities. Parameters associated with the normative model of WIVID contrast change reflect established patterns of region-specific and modality-specific maturational sequences. We also detect differences in WIVID contrast change trajectories between distinct population groups. These groups are categorized based on sex and risk/diagnosis for ASD (Autism Spectrum Disorder). As a result of this work, the usage of the proposed WIVID contrast measure as a novel neuroimaging biomarker for characterizing tissue appearance is validated, and the clinical potential of the developed framework is demonstrated

The aetiology of pain in chronic midportion Achilles tendinopathy

Background Achilles tendinopathy (AT) is a common injury in athletes and sedentary individuals, which presents as pain and loss of function in the lower limb. Tendon pathology can exist without pain, but the hallmark of the condition is pain, which is classically of insidious onset, related to loading activity and often resistant to treatment. While the biology of pain in general is well described, the mechanisms of pain in AT are not fully understood. Most commonly, the nociceptive driver associated with AT is thought to be a result of the structural changes that occur in the tendon or the inflammatory cascades that occur in the pathological tendon and/or reflective of altered central pain mechanisms. Evidence from other chronic pain conditions also shows that genetic variation explains, at least in part, some of the heterogeneity observed in chronic pain conditions. The presentation of chronic Achilles tendon pain is variable and therefore it is reasonable to propose that this variability may be influenced by a genetic component. The absence of a definitive cause or mechanism of pain in AT is reflected in the plethora of treatment strategies available to manage it, most of which are not universally effective. In order to improve the management of pain in chronic AT, it is imperative that its mechanisms be better understood. Aims of the thesis The aims of this thesis were therefore to characterise Achilles tendon pain using other pain questionnaires, to investigate the relationship between structural changes and central pain mechanisms with self-reported tendon pain. Additionally, the thesis sought to evaluate the relationship between selected gene variants and pain in a cohort of recreational athletes with chronic Achilles tendinopathy using a candidate gene approach. Candidate genes: COMT rs4818 (C/G), COMT rs4633 (C/T), TAC1rs2072100 (C/T), TACR1 rs3771829 (C/G) and SCN9A rs6746030 (G/A) were selected based on the biological function of their encoded proteins within the pain pathways. The objectives of the specific chapters which addressed these aims were: • Describe Achilles tendon pain using multidimensional pain scales; the short forms of the McGill pain questionnaire (sf-MPQ) and Brief Pain Inventory (sf-BPI), as well as the Victorian Institute of Sports Assessment – Achilles questionnaire (VISA-A) (Chapter 2). • Evaluate the relationship between self- reported tendon pain, the grey scale ultrasound and colour Doppler characteristics in chronic AT (Chapter 3). • Evaluate the relationship between conditioned pain modulation and chronic AT (Chapter 4). • Explore and evaluate if variants in genes [COMT rs4818 (C/G), COMT rs4633 (C/T), TAC1 rs2072100 (C/T), TACR1 rs3771829 (C/G) and SCN9A rs6746030 (G/A)] involved in the pain pathways are associated with either self-reported tendon pain and/or conditioned pain modulation (Chapter 5). Methods Two hundred and eighty-two (282) recreational athletes with at least one year's experience in their main sport were recruited for the studies in this thesis but fifty-two (52) were excluded for not meeting the inclusion criteria of the studies. Hence, 103 recreational athletes without a history of chronic AT (CON) and 127 participants clinically diagnosed with chronic AT (TEN) were included in the study. All participants completed demographic questionnaires on their medical, sporting, training, and injury history. Participants with AT (TEN) also completed the self-administered eight question VISA-A questionnaire, the sf-MPQ and the sf-BPI. Additionally, all participants had grey scale ultrasound (US) and colour Doppler (CD) assessments of both their tendons performed and had conditioned pain modulation (CPM) assessed using pressure and cold pain. Lastly, participants were genotyped for variants in COMT rs4818 (C/G), COMT rs4633 (C/T), TAC1 rs2072100 (C/T), TACR1 rs3771829 (C/G) and SCN9A rs6746030 (G/A) using standard PCR methods. Data were analysed using Statistica Version 13.2.50. Normality of data was assessed using the Shapiro-Wilks test. Evaluations of differences between normally distributed quantitative data were conducted with the independent students t-test or one-way ANOVA, while Mann-Whitney-U and Kruskall-Wallis tests were used for non-normally distributed data. The Fisher's exact and χ2 tests were used for categorical data. For post-hoc analyses, the Kruskal-Wallis associated multiple comparisons test with Bonferroni adjustment was used for quantitative data. For the genotyping data, Hardy– Weinberg equilibrium (HWE) was calculated using ‘HardyWeinberg' version 1.6.3. package. The overall level of significance was set at p0.3; p0.05). However, the median interference index scores of the VISA-A questionnaire of participants with US abnormalities [median (IQR)] [35.5 (30.0 - 41.0), n=36] was significantly higher than those without US abnormalities [32.5 (26.0 - 37.0), n=39, p=0.046]. Additionally, participants from the TEN group who reported no stabbing pain, those who reported mild, moderate or severe stabbing pain on the sf-MPQ had significantly thicker tendons [median (IQR)] [6.0mm (5.2 - 7.6) vs 7.0mm (5.9 - 8.9), 7.7mm (6.2 - 9.1) and 6.3mm (4.9 - 7.4), p=0.037]. From the CPM analysis, participants with tendinopathy had a lower pressure pain threshold (PPT) before [median (IQR)] [TEN: 417kPa (364 - 516) vs CON 601kPa (459 - 724), p<0.001] and during [TEN: 458kPa (358 - 550) vs CON 633kPa (506 - 753), p<0.001] the cold pressor test. However, there was no difference in the CPM effect between the two groups [median (IQR)] [TEN: 34kPa (-2 - 79) vs CON: 45kPa (4 - 94), p=0.490]. From the sf-BPI, PPT before the cold pressor test were significantly lower in individuals who reported mild to severe interferences in mood (p=0.023), general activity (p=0.038) and walking ability (p=0.004) when compared to those who reported no interferences. Pressure pain thresholds before the cold pressor test were also significantly lower in those participants who reported mild to severe pain at the time of testing (p=0.024) or reported moderate to severe pain on average (p=0.014) on the sf- BPI. Additionally, from the sf-BPI, a low CPM effect was significantly associated with mild to severe interference with sleep (p=0.043). The genotype analysis showed that the median total scores of self-reported tendon pain from the sf-MPQ were significantly different (p=0.019) among the three COMT rs4818 (G/C) genotype groups [median (IQR)] [CC: 9.1 (4.0 - 13.0) n=61; CG: 7.3 (4.0 - 0.0) n=50; GG: 4.0 (1.0 - 5.0) n=7], with the CC genotype having a significantly higher pain score (p=0.018) than the GG genotype. No other associations were observed between genotype distributions of COMT rs4633, TAC1 rs2072100, TACR1 rs3771829, SCN9A rs746030 and the median self-reported total tendon pain scores for the sf-MPQ, sf-BPI, VISA-A, or their subscales. Conclusion The novel findings of this thesis suggest that the language of chronic AT pain ought to be further investigated as it may help extend our knowledge of the underlying mechanisms in chronic AT pain. In addition, that AT pain interferes with more than physical and sporting ability should be considered in the overall management of this condition in athletes. While no associations were observed between imaging findings and tendon pain, the relationship between imaging findings and physical limitations suggests that using pain as a primary outcome measure in rehabilitation may be insufficient and highlights the need to further study the relationship between tendon structure, imaging and pain. Furthermore, impaired CPM was associated with interferences with sleep which suggests that, though not quite clear, some central mechanisms are at play in chronic AT pain. This finding also reaffirms the need to consider factors other than physical function in AT management. Another novel finding of this thesis was the association between COMT rs4818 (C/G) and chronic tendon pain. This finding suggests that the catecholaminergic pathway is involved in the chronic AT pain pathway. COMT variants are associated with maladaptive coping mechanisms which may be important to consider in managing chronic pain conditions such as AT. In future, larger studies are required in order to replicate these findings and large, prospective cohort studies are required to confirm the role of genetic variation in chronic AT pain. Overall, the mechanisms of pain in tendinopathy are complex and not yet well described, emphasising the further need for multi-sectorial research

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies