Accurate Hippocampus Segmentation Based on Self-supervised Learning with Fewer Labeled Data

Abstract

Brain MRI-based hippocampus segmentation is considered as an important biomedical method for prevention, early detection, and accurate diagnosis of neurodegenerative disorders like Alzheimer’s disease. The recent need for developing accurate as well as robust systems has led to breakthroughs making advantage of deep learning, but requiring significant amounts of labeled data, which, in turn, is costly and hardly obtainable. In this work, we try to address this issue by introducing self-supervised learning for hippocampus segmentation. We devise a new framework, based on the widely known method of Jigsaw puzzle reassembly, in which we first pre-train using one of the unlabeled MRI datasets, and then perform a downstream segmentation training with other labeled datasets. As a result, we found our method to capture local-level features for better learning of anatomical information pertaining to brain MRI images. Experiments with downstream segmentation training show considerable performance gains with self-supervised pre-training over supervised training when compared over multiple label fractions.