Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit

Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis

MRI plays an important role in the continuing search for a sensitive osteoarthritis (OA) imaging biomarker able to detect early, pre-morphological alterations in cartilage composition. Determining the compositional recovery pattern of cartilage following acute joint loading could potentially present a more sensitive biomarker for defining cartilage health [1]. However, only a limited amount of studies have assessed both the immediate effect of joint loading on cartilage, as well as its post-loading recovery. In addition, when assessing the compositional responses of cartilage to joint loading, previous studies usually did not incorporate the measurement error of the used quantitative MRI technique into their analysis. Therefore, an uncertainty persists whether or not compositional MRI techniques are sensitive enough to measure changes in water and macromolecular content of cartilage, or if previous studies were merely measuring noise. Consequently, an objective of this thesis is to increase our understanding of and reliability in quantitative T2 and T1ρ relaxation time mapping to detect compositional responses of cartilage following a joint loading activity. Furthermore, to obtain the quantitative morphological and compositional measures of cartilage, detailed region-specific delineation of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a time-efficient semi- or fully-automated alternative to this process [2, 3]. This thesis explores the utility of deep CNNs generated segmentations for accurate surface-based analysis of cartilage morphology and composition from knee MRIs as well as of cortical bone thickness from knee CTs. Chapter 1 will provide an introduction into the structure and biomechanics of articular cartilage and the role of MRI in imaging the degenerative joint disorder, osteoarthritis as well as the effects of different joint loading activities on cartilage morphology and composition. Chapter 2 explains the principle of MRI and the pulse sequences used in the following chapter for the morphometric and compositional assessment of articular cartilage. Chapter 3 describes the use of 3D Cartilage Surface Mapping (3D-CaSM) [3] to assess variations in cartilage T1ρ and T2 relaxation times of young, healthy participants following a mild, unilateral stepping activity. By evaluating and incorporating the intrasessional repeatability of the T1ρ and T2 mapping techniques, I aim to highlight those cartilage areas experiencing exercise-induced compositional changes greater than measurement error. A significant amount of time is needed to manually segment the regions-of-interest required to perform the 3D-CaSM used in Chapter 3. Therefore, in Chapter 4, I assessed the use of deep convolutional neural networks for automating the segmentation process for multiple knee joint tissues simultaneous and increase the time-efficiency for evaluating knee MR datasets. I evaluated the use of a conditional Generative Adversarial Network (cGAN) as a potentially improved method for automated segmentation compared to the widely used convolutional neural network, U-Net. In Chapter 5 I combined the 3D-CaSM and automated segmentation methods presented in Chapters 3 and 4, respectively to assess the use of fully automatic segmentations of femoral and tibial bone-cartilage structures for accurate surface-based analysis of cartilage morphology and composition on knee MR images. This was performed on publicly available data from the Osteoarthritis Initiative, a multicentre observational study with expert manual segmentations provided by the Zuse Institute in Berlin. Chapter 6 describes an automated pipeline for subchondral cortical bone thickness mapping from knee CT data. I developed a method of using automated segmentations of articular cartilage and bone from knee MRI data to determine the periarticular bone surface which is covered by cartilage. This surface was then used to perform cortical bone thickness measurements on corresponding CT data. I validated this pipeline using data from the EU-funded, multi-centre observational study called Applied Private-Public partneRship enabling OsteoArthritis Clinical Headway (APPROACH). Chapter 7 summarises the main conclusions and contributions of the works presented in this thesis as well as providing directions for future work.PhD Studentship funded by GlaxoSmithKlin

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.

Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation compared to other extensively used convolutional neural network, such as the U-Net. As cGANs have not yet been widely explored for semantic medical image segmentation, we analysed the effect of training with different objective functions and discriminator receptive field sizes on the segmentation performance of the cGAN. Additionally, we evaluated the possibility of using transfer learning to improve the segmentation accuracy. The networks were trained on i) the SKI10 dataset which comes from the MICCAI grand challenge "Segmentation of Knee Images 2010″, ii) the OAI ZIB dataset containing femoral and tibial bone and cartilage segmentations of the Osteoarthritis Initiative cohort and iii) a small locally acquired dataset (Advanced MRI of Osteoarthritis (AMROA) study) consisting of 3D fat-saturated spoiled gradient recalled-echo knee MRIs with manual segmentations of the femoral, tibial and patellar bone and cartilage, as well as the cruciate ligaments and selected peri-articular muscles. The Sørensen-Dice Similarity Coefficient (DSC), volumetric overlap error (VOE) and average surface distance (ASD) were calculated for segmentation performance evaluation. DSC ≥ 0.95 were achieved for all segmented bone structures, DSC ≥ 0.83 for cartilage and muscle tissues and DSC of ≈0.66 were achieved for cruciate ligament segmentations with both cGAN and U-Net on the in-house AMROA dataset. Reducing the receptive field size of the cGAN discriminator network improved the networks segmentation performance and resulted in segmentation accuracies equivalent to those of the U-Net. Pretraining not only increased segmentation accuracy of a few knee joint tissues of the fine-tuned dataset, but also increased the network's capacity to preserve segmentation capabilities for the pretrained dataset. cGAN machine learning can generate automated semantic maps of multiple tissues within the knee joint which could increase the accuracy and efficiency for evaluating joint health.European Union's Horizon 2020 Framework Programme [grant number 761214] Addenbrooke’s Charitable Trust (ACT) National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre University of Cambridge Cambridge University Hospitals NHS Foundation Trust GSK VARSITY: PHD STUDENTSHIP Funder reference: 300003198

Echo state network‐based feature extraction for efficient color image segmentation

Image segmentation plays a crucial role in many image processing and understanding applications. Despite the huge number of proposed image segmentation techniques, accurate segmentation remains a significant challenge in image analysis. This article investigates the viability of using echo state network (ESN), a biologically inspired recurrent neural network, as features extractor for efficient color image segmentation. First, an ensemble of initial pixel features is extracted from the original images and injected into the ESN reservoir. Second, the internal activations of the reservoir neurons are used as new pixel features. Third, the new features are classified using a feed forward neural network as a readout layer for the ESN. The quality of the pixel features produced by the ESN is evaluated through extensive series of experiments conducted on real world image datasets. The optimal operating range of different ESN setup parameters for producing competitive quality features is identified. The performance of the proposed ESN‐based framework is also evaluated on a domain‐specific application, namely, blood vessel segmentation in retinal images where experiments are conducted on the widely used digital retinal images for vessel extraction (DRIVE) dataset. The obtained results demonstrate that the proposed method outperforms state‐of‐the‐art general segmentation techniques in terms of performance with an F‐score of 0.92 ± 0.003 on the segmentation evaluation dataset. In addition, the proposed method achieves a comparable segmentation accuracy (0.9470) comparing with reported techniques of segmentation of blood vessels in images of retina and outperform them in terms of processing time. The average time required by our technique to segment one retinal image from DRIVE dataset is 8 seconds. Furthermore, empirically derived guidelines are proposed for adequately setting the ESN parameters for effective color image segmentation

Tissue Parameter Mapping in Children with Fetal Alcohol Spectrum Disorders

Background: Fetal alcohol spectrum disorders (FASD), which are caused by prenatal alcohol exposure (PAE), affects people around the world. Certain communities in South Africa have among the highest reported incidences of fetal alcohol syndrome (FAS) in the world. Although PAE-related brain alterations have been widely documented, the mechanisms whereby alcohol affects the brain are not clearly understood. MRI relaxation parameters T1, T2, T2* and proton density (PD), are basic tissue properties that reflect the underlying biology. The present study aims to advance our understanding of how PAE alters the microstructural properties of tissue by examining PAE-related changes in these tissue parameters in adolescents with FASD. Methods: The final sample used in this study consisted of 53 children from a previously studied longitudinal cohort (Jacobson et al., 2008) and 12 additionally recruited subjects. Of the 65 participants, 18 were diagnosed with FAS or partial FAS (PFAS) and made up the FAS/PFAS group, 18 were diagnosed as heavily exposed non-syndromal (HE) and 29 were age matched controls. Subjects were scanned at the Cape Universities Body Imaging Centre (CUBIC) located at Groote Schuur Hospital on a 3T Siemens Skyra MRI. Structural images were obtained using the MEMPRAGE sequence. From these images T1, T2, T2* and PD parameter maps were constructed and segmented into 43 regions of interest (ROI) using Freesurfer, FSL and AFNI. Linear regression analyses were used to analyse group differences as well as correlations between parameter values and the amount of alcohol the mother consumed during pregnancy. Results: Significant T1 differences were found in the caudate, cerebellar cortex, hippocampus, accumbens, putamen, choroid plexus, ventral diencephalon (DC), right vessel and ventricles. Significant T2 differences were found in the caudate, brain stem, corpus callosum (CC), amygdala, cerebral cortex, choroid plexus, vessels and ventricles. Significant T2* differences were found in the cerebellar cortex, optic chiasm and ventricles. Significant PD differences were found in the hippocampus and left lateral ventricle. The exploratory nature of this study resulted in none of the results surviving FDR correction for multiple comparisons. Conclusions: Overall, our findings point to regional PAE-related increases in water content and cellular and molecular changes in underlying tissue of the anatomical structure. Exceptions were the right cerebral cortex, brain stem, hippocampus, amygdala and ventral diencephalon where our findings point to less free water and increased cell density, and cerebellar cortex where simultaneous reductions in T1 and T2* suggest the possibility of increased iron content. In highly myelinated white matter structures, such as the CC and optic chiasm, our results point to PAErelated demyelination, and possibly increased iron. These findings extend previous knowledge of effects of PAE and demonstrate that tissues are affected at a microstructural level

Characterising the Pore Space of Selected Sandstone Samples using Multiple Approaches

A comprehensive knowledge of the porosity and pore size distribution (PSD) of hydrocarbon reservoirs is vital to several petroleum engineering disciplines including reserve estimation, reservoir characterisation, drilling operations and reservoir development planning. This work examines the three methods of Mercury Injection Capillary Pressure (MICP) Testing, Pore Network Modelling (PNM) and Nuclear Magnetic Resonance (NMR) which are currently used within the petroleum industry to determine representative measures of porosity and PSD. Although MICP is a common method used within the petroleum industry, several factors impact its suitability for determining porosity and PSD. These are related to the destructive nature of the test which is challenging when samples are limited in quantity as well as the limitations of MICP to provide robust results for certain kinds of reservoir material, particularly for those that are unconsolidated and unconventional. Recent advances in PNM and NMR have made these approaches attractive alternatives for pore evaluation studies which can enhance, supplement or replace the information derived from MICP testing. To examine the applicability of PNM and NMR methods to determine porosity and PSD, three sandstone core samples were used throughout this study. These were the Berea and Bentheimer core samples, which are consolidated and homogenous in nature allowing an opportunity for the benchmark testing of the PNM and NMR approaches and an Athabasca Oil Sand (AOS) sample, which is a prime example of unconsolidated material containing a very viscous in-situ fluid. During the PNM process, micro-computed tomography (micro-CT) was used to obtain 2D contiguous images of a sample which were then compiled to produce a 3D representation of the pore space. Based on the literature, a 12-step comprehensive PNM approach was developed in this work and applied to the benchmark Berea and Bentheimer core samples to derive their porosity and PSD. This had a substantial processing time of over 100 hours (> 4 days) for each sample. The key findings from this comprehensive approach formed the basis of a simplified recommended PNM practice having only 9 steps and an anticipated processing time of 7 hours and 26 hours for homogenous and heterogeneous samples respectively. This simplified recommended PNM practice was then applied to the AOS sample with the porosity and PSD results showing a good agreement to that from the MICP and NMR testing. The determination of porosity and PSD from NMR testing requires specific fluids to be contained in the pore space. This generally involves the removal and replacement of all original fluids with water (or brine) since the response of the low-viscosity water correlates well with surface measurements of the pore space. This typically precludes the testing of samples imbued with their original fluids which poses several restrictions for the NMR testing of unconsolidated and partially consolidated material. The development of techniques which allow for the robust pore space testing of these kinds of materials without the cleaning or removal of their native fluids is therefore valuable to the petroleum industry. Based on these ideas, a novel empirical transform was developed which could allow the NMR testing of samples containing viscous fluids. This transform used the NMR response of glycerol (which is 1,412 times more viscous than water at 20oC) to develop a transform based on viscosity. The use of this transform showed great success in obtaining a robust PSD for the AOS sample containing its native bitumen which is comparable to the PSDs from the MICP and PNM approaches. These results indicate that the PNM and NMR approaches can provide comparable results to conventional MICP testing, that they can be used as independent techniques for evaluating the pore space and that they can provide a robust measurement of the porosity and PSD for samples imbued with their native hydrocarbon fluids. When compared to MICP testing, these approaches might be preferred when testing a limited quantity of core samples, partially consolidated and unconsolidated samples and samples containing their original fluids. Future work to strengthen these results include using a wider range of sandstone samples to test the developed recommended PNM practice and using a wider range of samples containing a greater variety of fluids to test the empirical transform

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