Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Advances in Analysis and Exploration in Medical Imaging

With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data. A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes. Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach. The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd