Automated 3D Axonal Morphometry of White Matter

Peer reviewe

gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy

Background and Objective: Advances in electron microscopy (EM) now allow three-dimensional (3D) imaging of hundreds of micrometers of tissue with nanometer-scale resolution, providing new opportunities to study the ultrastructure of the brain. In this work, we introduce a freely available Matlab-based gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes of brain tissue samples. Methods: The software is equipped with a graphical user interface (GUI). It automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths and allows manual segmentation, proofreading, and interactive correction of the segmented components. gACSON analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or gratio. Results: We illustrate the use of the software by segmenting and analyzing myelinated axons in six 3DEM volumes of rat somatosensory cortex after sham surgery or traumatic brain injury (TBI). Our results suggest that the equivalent diameter of myelinated axons in somatosensory cortex was decreased in TBI animals five months after the injury. Conclusion: Our results indicate that gACSON is a valuable tool for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes. It is freely available at https://github.com/AndreaBehan/g-ACSON under the MIT license. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )Peer reviewe

Bioengineering Analysis of Traumatic Brain Injury

Traumatic brain injury (TBI) is a serious health concern affecting over a million people in the UK. Brain shift and herniation, which are closely related to severe disability or death, are important signs of abnormally elevated intracranial pressure (ICP) or space-occupying intracranial mass after trauma. This research aims to use medical image computing and biomechanical modelling techniques to characterise the specific deformation field of brain tissues under various TBI scenarios and strengthen the biomechanical understanding across the full spectrum of TBI. Medical image computing provides the research with a solid clinical grounding. To better interpret the neuro-images, three computational tools have been developed, including a CT preprocessing pipeline, an automatic mid-sagittal plane detector and an automatic brain extractor. Using these tools, a novel concept of midplane shift (MPS) is developed to quantitatively evaluate the brain herniation condition across the mid-sagittal plane. In the meantime, a lesion heatmap is generated to quantify the asymmetric haematoma volumes across the mid-sagittal plane. The MPS heatmaps generated for 33 TBI patients with heterogeneous brain pathologies demonstrate highly similar shift patterns. Together with the lesion heatmap, a brain deformation mechanism has been presented: the brain will not deform randomly in response to trauma, instead, it will only deform in a regulated mechanism so that the deformation is directed and restricted to the soft ventricular region, thanks to the anatomic structures of the head such as the falx. The MPS heatmap, the lesion heatmap, together with the novel CT parameters derived from them, provide a rich abundance of information on intracranial brain herniation, for a more complete overview of TBI from medical images. Biomechanical modelling, being one of the most important tools in trauma biomechanics, has been used to quantitatively simulate the brain shift and herniation condition caused by various intracranial lesions and increasing ICP. Preliminary finite element models reconstructed from the Virtual Human Project have demonstrated some limitations. To resolve the observed deficiencies, an advanced high-fidelity patient-specific FE brain model is constructed and explicitly assessed to optimise its injury simulation performance with the help of the developed medical image computing tools. During simulation, the patient-specific traumatic injuries have been reconstructed by imposing both the primary lesion and the secondary injury. The primary lesion simulation is achieved mechanically by ``indenting" a rigid lesion surface simulating the shape of the haematoma to the brain model. While the secondary swelling is modelled with a thermal-expansion-based method to simulate the bulging brain. Using this approach, the observed brain herniation can be decomposed into a deformation due to pure mass effect of space-occupying primary lesion and a shift as a result of secondary swelling. The head injuries of six different TBI patients have been reconstructed and simulated using the prescribed method. The realistic case study suggested that the subdural haematoma patients, as compared to the epidural haematoma patients, were exposed to more significant secondary swelling, which agrees well with the historical clinical findings. In addition to the realistic TBI case studies, an idealised traumatic lesion simulation is performed to investigate the role of lesion morphology and the lesion locations of onsets, in brain herniations during TBI. It is suggested by the idealised TBI cases that the brain is more sensitive to lesion that is more concentrated spatially, if lesion volumes and lesion locations were exactly the same. Moreover, in terms of lesion locations, lesions that strikes on the temporal region and the anterior region are more likely to lead to greater brain deformation, if other lesion morphologies were equal and no secondary swelling considered. Ultimately, the developed tools are expected to help clinicians better understand and predict the brain behaviour after the onset of TBI and during subsequent injury evolution.WD Armstrong Trus

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

DeepACSON automated segmentation of white matter in 3D electron microscopy

Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury. With DeepACSON, Abdollahzadeh et al. combines existing deep learning-based methods for semantic segmentation and a novel shape decomposition technique for the instance segmentation. The pipeline is used to segment low-resolution 3D-EM datasets allowing quantification of white matter morphology in large fields-of-view.Peer reviewe

Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies