U-net in constraint few-shot settings: enforcing few-sample-fitting for faster convergence of u-net for femur segmentation in X-ray

Abstract

In this paper, we investigate the feasibility of using a standard U-Net for Few-Shot segmentation tasks in very constraint settings. We demonstrate on the example of femur segmentation in X-ray images, that a U-Net architecture only needs few samples to generate accurate segmentations, if the images and the structure of interest only show little variance in appearance and perspective. This is often the case in medical imaging. We also present a novel training strategy for the UNet, leveraging U-Net’s Few-Shot capability for inter-patient consistent protocols. We propose repeatedly enforcing Few-Sample-Fitting the network for faster convergence. The results of our experiments indicate that incrementally fitting the network to an increasing sample set can lead to faster network convergence in constraint few-shot settings.