[OA214] An experience with open source machine learning software deepmedic

Purpose The aim of this study was to investigate if 3D convolutional deep neural network implementation DeepMedic [1] could be applied to computed tomography angiography (CTA) images in acute stroke lesion detection. DeepMedic has previously been successful in lesion segmentation from magnetic resonance images [2] . Methods Preprocessing steps included robust intracranial space segmentation and intensity normalization. Hypoattenuated regions visible in the CTAs were manually delineated by two neuroradiologists and considered as ground truths. Data augmentation was performed with left–right flipped images. Training was done with scans from five infarct patients and subsequent testing with five separate cases. 35 epochs with 15 subepochs were used in the training. Results The initial results were promising as the infarcts in all the test cases could be correctly lateralized. The achieved voxel-wise segmentation performance was moderate (considering small training set) with Sorensen-Dice similarity coefficient 0.52 for the test data. Conclusions The significance of open science and freely available scientific software will only increase in the future. Convolutional neural networks demonstrate promising prospects for medical imaging. DeepMedic can be applied to CT images with proper data preprocessing. In a future study the findings will be verified with a larger cohort. Improving the performance with additional input features will also be investigated.