Towards Non-invasive Image-Based Early Diagnosis of Autism
The ultimate goal of this paper is to develop a computer-aided diagnostic (CAD) system for the accurate and early diagnosis of autism spectrum disorders (ASDs) using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the
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Bioengineering Department, University of Louisville, Louisville, KY, USA [email protected] 2 Department of Psychiatry and Behavioral Science, University of Louisville, Louisville, KY, USA Department of Computer Science, University of Auckland, Auckland, New Zealand Abstract. The ultimate goal of this paper is to develop a computeraided diagnostic (CAD) system for the accurate and early diagnosis of autism spectrum disorders (ASDs) using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using firstand second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 38 infants with a high risk of developing ASDs. The statistical analysis and the diagnostic results (87% accuracy and AUC of 0.96 using random forest classifier) confirm the high performance and the efficiency of the proposed CAD system. Keywords: Infant, Tissue segmentation, DTI, Subject-specific atlas.
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Introduction
Autism spectrum disorders (ASDs) are a group of lifetime developmental disabilities that are defined by significant social, communication, and behavioral challenges. Currently, ASDs denote a significant growing public health concern. According to the report issued by the Centers for Disease Control and Prevention (CDC) in 2014, one in 68 children has been diagnosed with ASDs in the United States, which is approximately 30% greater than previous estimates in 2012 of one in 88 children [1]. Thus, there is an urgent need for a non-invasive technology with the capability of providing new laboratory-based measures that confer an early and accurate diagnosis of ASDs. Recent molecular and functional connectivity studies indicated that brain connectivity and the underlying white matter tracts might be impaired in patients
Corresponding author.
c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part II, LNCS 9350, pp. 160–168, 2015. DOI: 10.1007/978-3-319-24571-3_20
Towards Non-invasive Image-Based Early Diagnosis of Autism
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with ASDs, but these studies failed to provide sufficient information about the morphology characteristics of these white matter tracts [2]. On the other hand, structural studies were based on extracting volumetric [3] or shape [4] information from the brain to detect differences between control and autistic patients. However, most of these studies were age sensitive and failed to detect abnormalities in infant brains [5]. Fortunatel
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