教師なし変形可能な超音波画像レジストレーションと血管セグメンテーションへの応用
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation
RAFTをベースにした教師なし超音波画像レジストレーションモデルU-RAFTを提案し、力制御ロボットスキャンと組み合わせて合成変形画像を生成、血管セグメンテーションのデータ拡張に活用してIoUを改善した。
著者: FNU Abhimanyu, Andrew L. Orekhov, Ananya Bal, John Galeotti, Howie Choset
分類: eess.IV, cs.CV, cs.RO
原文アブストラクト
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
