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Image-based Autism Diagnostic Systems (11064, 12063, & 13098)

Detailed Technology Description
None
*Abstract

     

       

Features and Benefits

  • Computer-assisted diagnostic tool for accurate identification of autism
  • Enables fast and accurate shape analysis of the human brain
  • Fully-automated and highly accurate segmentation of both adult and infant brain images
  • Expected to facilitate earlier diagnosis, intervention, and, in turn, improved functional outcomes for individuals

      

*This technology portfolio is available for licensing, further development, or industrial partnering

      

Technology

     

University of Louisville researchers are developing a technology portfolio that utilizes 3D non-contrast MR imaging (MRI) to detect and develop a comprehensive analysis of neurological conditions, such as autism, dyslexia, and ADHD.

    

The initial focus of this project has been the development of a computer-assisted diagnostic software tool to identify individuals with autism spectrum disorder (ASD). The method for classifying individuals is comprised of three primary steps. First, the cerebral cortex is segmented from T1-weighted MR images using an atlas-guided visual appearance model. A hybrid framework was developed for segmenting brain images in both infants and adults. The hybrid framework accounts for numerous variations in individual patient brain matter to accurately segment the brain tissue.

      

     

Following segmentation, a 3D mesh manifold of the cerebral cortex is constructed and aligned to a reference atlas. The 3D mesh manifold then undergoes a detailed shape analysis that examines individual locations on the surface of the brain to isolate differences in the cerebral cortex gyrifications (folds of the brain). The shape analysis is used to construct a classification and risk analysis for individuals.

     

This method has yielded promising results in early studies and has potential to aid in the diagnosis of ASD at a critical early stage, where therapy and treatment can have a greater impact on individuals.

      

Markets Addressed

     

Currently, early diagnosis of autism spectrum disorder (ASD) is dependent on recognizing behavioral and developmental indications, which can be difficult to recognize for parents and clinicians. Other research and development efforts directed to early diagnosis of ASD are generally focused on genetic biomarkers, which have been difficult to concretely identify due to the wide range of individuals within the autism spectrum. This method of classifying individuals with ASD based on analyzing the shape of cerebral cortex could provide an objective alternative for recognizing behavioral and developmental indicators. The method, which utilizes non-contrast MR imaging, is also safer than invasive methods such as blood-based tests.

      

Technology Status

      

IP Status:

  • U.S. Patent No. 9,230,321, "Computer aided diagnostic system incorporating 3D shape analysis of the brain for identifying developmental brain disorders," ULRF Ref. 11064
  • U.S. Patent Application No. 15/233,671, "Computer aided diagnostic system for mapping of brain images," ULRF Ref. 13098
  • U.S. Copyright Registration No. TX 7-520-124 for "Mesh diagnostic software," ULRF Ref. 12063

      

Development Status:

  • A full software suite has been developed containing algorithms for bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification.
  • The software has been tested on a wide variety of data including infant, children, and adult data sets with accuracies ranging between 85%-97%.

       

Publications:

  • M. Nitzken, M. Casanova, G. Gimel'farb, T. Inanc, J. Zurada, and A. El-Baz, "Shape Analysis of the Human Brain: A Brief Survey," IEEE Transactions on Information Technology in Biomedicine, 2014, (DOI 10.1109/JBHI.2014.2298139), (in press).
  • M. Mostapha, A. Alansary, A. Soliman, F. Khalifa, M. Nitzken, M. Casanova, A. El-Baz. "Atlas-- Based Approach for The Segmentation of Infant DTI MR Brain Images." In Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'14), 2014. (in press).
  • B. Dombroski, M. Nitzken, A. Elnakib, F. Khalifa, A. El-Baz, M. Casanova, '' Cortical Surface Complexity in a Population-Based Normative Sample,'' Translational Neuroscience, vol. 5, 2014
  • A. Elnakib, A. Soliman, M. Nitzken, M. F. Casanova, G. Gimel'farb, and A. El-Baz, "Magnetic Resonance Imaging Findings for Dyslexia: A Review," Journal of Biomedical Nanotechnology, vol. 10, pp. 1–28, 2014 (in press).
  • A. Alansary, A. Soliman, F. Khalifa, A. Elnakib, M. Mostapha, M. Nitzken, M. Casanova, and A. El-Baz, "MAP–Based Framework for Segmentation of MR Brain Images Based on Visual Appearance and Prior Shape," MIDAS Journal, vol. 1, pp. 1-13, Oct 2013. Available: http://hdl.handle.net/10380/3440.
  • M. Nitzken, N. Bajaj, S Aslan, G Gimel'farb, A. El-Baz, A. Ovechkin, "Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury," Journal of Biomedical Science and Engineering, vol. 6, pp. 1-13, July, 2013. doi:10.4236/jbise.2013.67A2001
  • A. Elnakib, M. Nitzken, M. F. Casanova, H.-Y. Park, G. Gimel'farb, and A. El-Baz, "Quantification of Age-related Brain Cortex Change using 3D Shape Analysis," Proc. IEEE International Conference on Pattern Recognition (ICPR'12), Tsukuba, Japan, November 11–15, 2012, pp. 41-44.
  • M. Casanova, M. Nitzken, E. Williams, A. Switala, and A. El-Baz, "A Cerebral Spectrum From Autism to Dyslexia: Determining Cortical Surface Complexity Utilizing Spherical Harmonics," IMFAR Program Booklet & Abstracts, Toronto, Canada, May 17-19, 2012. 
  • E. Williams, A. El-Baz, M. Nitzken, A. Switala, and M. Casanova, "Spherical Harmonic Analysis of Cortical Complexity in Autism and Dyslexia," Translational Neuroscience, vol. 3, no. 1, pp. 36-40, March 2012.
  • A. El-Baz, M. Nitzken, G. Gimel'farb, E. Van Bogaert, R. Falk, M. Abo El-Ghar, and J. Suri, "3D Shape Analysis Using Spherical Harmonics for Early Assessment of Detected Lung Nodules," In: Handbook of Lung Imaging and Computer Aided Diagnosis, Chapter 19, (A. El-Baz and J. Suri, Eds.), Taylor & Francis, October 2011, ISBN:9781439845578, ISBN 10:1439845573.
  • M. Nitzken, M. Casanova, F. Khalifa, G. Sokhadze, and A. El-Baz, "Shape-Based Detection of Cortex Variability for More Accurate Discrimination Between Autistic and Normal Brains," In: Handbook of Multi-Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, Volume 2, Chapter 7, (A. El-Baz, R. Acharya, A. Laine, and J. Suri, Eds.), Springer-Verlag, New York, March 2011, pp. 161- 185. (ISBN: 978-1-4419-8203-2)
  • M. Nitzken, M. Casanova, G. Gimel'farb, A. Elnakib, F. Khalifa, A. Switala, and A. El-Baz, "3D ShapeAnalysis of the Brain Cortex with Application to Dyslexia," Proc. IEEE International Conference on Image Processing (ICIP'11), Brussels, Belgium, September 11-14, 2011, pp. 2713–2716. (Selected for oral presentation. Oral acceptance rate is 10% and the overall acceptance rate is 35%).
  • M. Nitzken, M. Casanova, G. Gimel'farb, F. Khalifa, A. Elnakib, A. Switala, and A. El-Baz, "3D Shape Analysis of the Brain Cortex with Application to Autism," Proc. of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'11), Chicago Illinois, USA, March 30–April 2, 2011, pp. 1847-1850.
Country/Region
USA

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