Autism diagnostics by centerline-based shape analysis of the Corpus Callosum

Elnakib, Ahmed; Gimel’farb, Georgy; Switala, Andrew E.; El-Baz, Ayman

doi:10.1109/isbi.2011.5872766

Cited by 23 publications

(10 citation statements)

References 20 publications

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“…The mean classification accuracy using poses, shapes, and a combination of poses and shapes was 56%, 60%, and 64%, respectively, for an image Elnakib et al [47] obtained notably better classification accuracy, using a method shown in Figure 3.7, for autistic and control subjects by analyzing the corpus callosum centerline:…”

Section: Medial Axis and Skeletal Analysis Chapter 3 Survey Of Shapementioning

confidence: 98%

“…Egaas et al [45], Schumann et al [46], and Elnakib et al [47] all implicated the corpus callosum in the development of autism. The corpus callosum is the largest single fiber bundle in the brain, being responsible for connecting the two hemispheres of the brain.…”

Section: Neuropathology Of Autism Spectrum Disordersmentioning

confidence: 99%

“…Year Mode Dim # GT Naf et al [80] 1996 A 3D n/a N Golland et al [81] 1999 A 2D 66 C Pizer et al [82] 1999 SA 2D 20 C Golland et al [83] 2001 A 3D 30 C Styner et al [84] 2001 A 3D 20 C Gorczowski et al [85] 2007 A 3D 70 C Elnakib et al [47] 2011 A 3D 34 C Paniagua et al [86] 2013 A 3D 90 C Table 3.1 indexes applications of skeletons for human brain analysis, starting with the novel proposal by Naf et al [80]. Naf classified various organs, including the brain, after characterizing their structure in 3D images with Voronoi diagrams and skeletons.…”

Section: Publicationmentioning

confidence: 99%

“…• Methods such as medial axis analysis [47] and geodesic distances [104] are more frequently combined with other techniques, leading to more accurate segmentation, registration and classification of the human brain and its various subcomponents, including, the ventricles and corpus callosum.…”

Section: Comparisons and Trendsmentioning

confidence: 99%

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Shape analysis of the human brain.

Nitzken¹,

Joseph²

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DEDICATIONSometimes our light goes out but is blown into flame by another human being.Each of us owes deepest thanks to those who have rekindled this light. Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods:The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses v and therapeutic risk assessments for each patient.Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion:Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood.vi

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Section: Medial Axis and Skeletal Analysis Chapter 3 Survey Of Shapementioning

confidence: 98%

Section: Neuropathology Of Autism Spectrum Disordersmentioning

confidence: 99%

Section: Publicationmentioning

confidence: 99%

Section: Comparisons and Trendsmentioning

confidence: 99%

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Shape analysis of the human brain.

Nitzken¹,

Joseph²

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“…An approach to extract the centerline of the 3D CC is presented based on solving Equation (13) [9,[194][195][196][269][270][271][272][273]. The main steps of the centerline extraction approach are detailed in Algorithm 9.…”

Section: Centerline Extraction From the CCmentioning

confidence: 99%

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

Elnakib¹

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DEDICATIONThis dissertation is dedicated to my mother, my father, and my wife for their love, patience, and support during the completion of this endeavor.iii ACKNOWLEDGMENTSIn the Name of Allah, the most beneficent, the most merciful. All deepest thanks are due to Allah, the compassionate for the uncountable gifts given to me.I would like to express my sincere gratitude to Professor El-Baz, my dissertation advisor, for the immeasurable amount of support and guidance he has provided throughout this study. He gave me the opportunity to work in his distinguished research group and provided me with an exciting working environment that facilitate many opportunities to develop new ideas and working on promising applications. Dr. El-Baz is always willing to give, share, and appreciate. I knew that I could always expect full credit for my accomplishments. He always supported me with novel ideas that helped me to publish my works in the top international conference and journal papers. It is worthy to mention that Professor El-Baz is a great advisor as well as a good friend who helps any Ph.D. student who works with him in high energy level. I cannot thank him enough, and shall remain grateful for all he has done. 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 vi 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...

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Deep Learning for Medical Image Segmentation

Akraoui

Daoui

2021

Business Intelligence

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Autism diagnostics by centerline-based shape analysis of the Corpus Callosum

Cited by 23 publications

References 20 publications

Shape analysis of the human brain.

Shape analysis of the human brain.

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

Deep Learning for Medical Image Segmentation

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