Abstract #2455

Knowledge-based definition of morphological alterations in brain MRI through the angle-based thresholding approach

Yusuke Tomogane¹, Jill Chotiyanonta ¹, Can Ceritoglu², Kumiko Oishi², Michael I Miller², Susumu Mori¹, Kenichi Oishi¹, and for the Pediatric Imaging, Neurocognition and Genetic study³

¹The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³for the Pediatric Imaging, Neurocognition and Genetic study, multiple cities and states, CA, United States

An automated method to detect brain morphological alterations was developed, which was designed for clinical pediatric brain MRIs with heterogeneous clinical conditions. Numerous image-feature-recognition algorithms have successfully defined abnormalities related to specific diseases, but there has been little research into a method that could identify a wide-range of radiological findings that could vary depending on the type and severity of different pathologies. A proposed approach—structural image parcellation followed by an angle-based outlier detection (ABOD) algorithm—could identify mild morphological alterations with high sensitivity and excellent specificity, when applied to clinical pediatric brain MRIs.

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