Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation

TL;DR

This paper introduces U-RAFT, an unsupervised deep learning model for deformable ultrasound image registration that generates synthetic images to enhance vessel segmentation training datasets, validated on phantom and in-vivo data.

Contribution

The paper presents U-RAFT, a novel unsupervised neural network for ultrasound image registration capable of producing synthetic images for data augmentation in vessel segmentation.

Findings

U-RAFT achieves 98% SSIM on phantom images and 81% on porcine images.

Synthetic images from U-RAFT improve vessel segmentation IoU performance.

The approach enables dataset expansion without manual labeling.

Abstract

This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate synthetic images for training vessel segmentation models. We propose and compare the registration quality of different loss functions for training U-RAFT. We also show how our approach, together with a robot performing force-controlled scans, can be used to generate synthetic deformed images to significantly expand the size of a femoral vessel segmentation training dataset without the need for additional manual labeling. We validate our approach on both a silicone human tissue phantom as well as on in-vivo porcine images. We show that U-RAFT generates synthetic ultrasound images with 98% and 81% structural similarity index measure (SSIM) to the real ultrasound images for the phantom and porcine datasets, respectively. We also demonstrate that synthetic deformed images from U-RAFT can be used as a data augmentation technique for vessel segmentation models to improve intersection-over-union (IoU) segmentation performance