Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor.. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations in the 3D anatomical atlas. Next, we fine-tune it with 2D freehand ultrasound images using a novel unsupervised cycle consistency, which utilizes the fact that the overall displacement of a sequence of images in the 3D anatomical atlas is equal to the displacement from the first image to the last in that sequence. We demonstrate that AdLocUI can adapt to three different ultrasound datasets, acquired with different machines and protocols, and achieves significantly better localization accuracy than the baselines. AdLocUI can be used for sensorless 2D freehand ultrasound guidance by the bedside. The source code is available at https://github.com/pakheiyeung/AdLocUI.
翻译:双维(2D) 免费超声波是产前护理和胎儿生长监测的支柱。 将3D解剖中相应的截面机匹配到3D解剖图层中, 给定的 2D 超声波脑扫描是免费扫描所必需的, 但具有挑战性 。 我们提议 AdLocuI, 这个框架将 3D 解剖图集中的 2D 超声图集整体本地化, 而不使用任何外部跟踪传感器 。 我们首先训练一个具有 2D 切片 的神经神经网络, 从 3D 调合的 3D 超声波中取样, 以预测其位于 3D 解剖图集中的位置 。 下一步, 我们用 2D 免费超声波图集图像进行微调。 3D 解剖图解图集中图像序列的总体移位与从第一个图像移位到该序列中最后一个图像的移位相等 。 我们证明 AdLocuI 能够适应三个不同的超声道数据集, 用不同的机器和协议来预测它们的位置。 并且在2DADLUDLU 上实现高度自由化, 的精确度 。 。 。 。 用于 ASlodLUBLLUB 的 的 正在 正在 正在 正在 正在 正在 正在 正在 正在 正在 正在 的 正在 。