Transferring from ex-vivo to in-vivo: Instrument Localization in 3D Cardiac Ultrasound Using Pyramid-UNet with Hybrid Loss

Abstract

Automated instrument localization during cardiac interventions is essential to accurately and efficiently interpret a 3D ultrasound (US) image. In this paper, we propose a method to automatically localize the cardiac intervention instrument (RF-ablation catheter or guidewire) in a 3D US volume. We propose a Pyramid-UNet, which exploits the multi-scale information for better segmentation performance. Furthermore, a hybrid loss function is introduced, which consists of contextual loss and class-balanced focal loss, to enhance the performance of the network in cardiac US images. We have collected a challenging ex-vivo dataset to validate our method, which achieves a Dice score of 69.6% being 18.8% higher than the state-of-the-art methods. Moreover, with the pre-trained model on the ex-vivo dataset, our method can be easily adapted to the in-vivo dataset with several iterations and then achieves a Dice score of 65.8% for a different instrument. With segmentation, instruments can be localized with an average error less than 3 voxels in both datasets. To the best of our knowledge, this is the first work to validate the image-based method on in-vivo cardiac datasets.