Combination of CNN and Hand-crafted feature for Ischemic Stroke Lesion Segmentation

CNN can automatically learn discriminative local features and give superior performance than hand-crafted features in various applications such as image classification, semantic segmentation and object detection. CNN has also been applied to MRI brain image analysis and achieved state-of-the-art results for brain tumor region segmentation [3, 4], stroke lesion segmentation [4], and mircobleeds detection [2]. Recently, some studies (e.g. [5]) show that hand-crafted features may provide complementary information with CNN, hence combining them with the features extracted from CNN may give improved performance than only using the features from CNN. Motived by this, we formulate the segmentation of ischemic stroke lesion in acute MRI scans as a pixel-level classification using a combination of CNN and hand-crafted features.