Improving accuracy of automatic hippocampus segmentation in routine MRI by features learned from ultra-high field MRI

Abstract

Ultra-high field MR imaging (e.g., 7T) provides unprecedented spatial resolution and superior signal-to-noise ratio, compared to the routine MR imaging (e.g., 1.5T and 3T). It allows precise depiction of small anatomical structures such as hippocampus and further benefits diagnosis of neurodegenerative diseases. However, the routine MR imaging is still mainly used in research and clinical studies, where accurate hippocampus segmentation is desired. In this paper, we present an automatic method for segmenting hippocampus from the routine MR images by learning 7T-like features from the training 7T MR images. Our main idea is to map features of the routine MR image to be similar to 7T image features, thus increasing their discriminability in hippocampus segmentation. Specifically, we propose a patch-based mapping method to map image patches of the routine MR images to the space of image patches of the 7T MR images. Thus, for each patch in the routine MR image, we can generate a new mapped patch with 7T-like pattern. Then, using those mapped patches, we can use a random forest to train a sequence of classifiers for hippocampus segmentation based on the appearance, texture, and contexture features of those mapped patches. Finally, hippocampi in the test image can be segmented by applying the learned image patch mapping and trained classifiers. Experimental results show that the accuracy of hippocampus segmentation can be significantly improved by using our learned 7T-like image features, in comparison to the direct use of features extracted from the routine MR images.