Abstract T P176: Classification of DWI Lesion Patterns in Acute Ischemic Stroke Using Shape Context

Abstract

Introduction: Neuroimaging studies in ischemic stroke hold abundant features about location, type, and extent of brain ischemia. Identification of imaging characteristics that predict recovery of brain tissue at risk remains an ongoing effort and such DWI patterns hold promise. However, systematic analysis of large multi-modal datasets face many challenges. An automated algorithm for classification of DWI lesion patterns in real-time would be ideal. We developed and evaluated a computer vision model that uses 3D shape description of the lesion for classification of DWI lesion patterns into an existing nomenclature. Methods: Inclusion criteria were acute ischemic stroke and DWI performed within 24 hours of symptom onset. Acute lesions were manually segmented on DWI and categorized into 6 types (territorial, other cortical, small superficial, internal border zone, small deep, and other deep infarcts). The computer vision model characterized stroke lesion using a shape context descriptor that accumulates surface points of the lesion into a 3D log-linear spherical histogram. The experiments estimate the accuracy of the model in classifying the lesion patterns using a leave-one-out cross-validation. Results: A total of 344 patients satisfied inclusion criteria. Mean age was 66.1 (range 13-97). Median NIHSS at baseline was 14 (range 0-38). Average lesion volume was 75.6 cc (range 5-256). The number of observations per category was [territorial:81, other cortical:51, small superficial:78, internal border zone:12, small deep:34, other deep infarcts:88]. When combining lesion volume, average location relative to the center of the brain, and shape descriptor as input, the classification models yielded perfect classification accuracy over the entire dataset. Conclusions: An advanced computer vision framework that automatically identifies discriminant features between lesion categories was introduced and successfully evaluated on 344 patients with acute stroke. Our study enables even larger scale retrospective analyses. Computer vision and pattern recognition methods can play a central role for the systematic analysis of big data in stroke.