Abstract
This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use for more effective navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model utilizing both labeled SPs and unlabeled 3D US volume slices. Our model enables reliable segmentation across a diverse set of fetal brain images. Furthermore, the model incorporates a classification mechanism to identify the fetal brain precisely. Our model not only filters out frames lacking the brain but also generates masks for those containing it, enhancing the relevance of plane pose regression in clinical settings. We focus on fetal brain navigation from 2D ultrasound (US) video analysis and combine this model with a US plane pose regression network to provide sensorless proximity detection to SPs and non-SPs planes; we emphasize the importance of proximity detection to SPs for guiding sonographers, offering a substantial advantage over traditional methods by allowing earlier and more precise adjustments during scanning. We demonstrate the practical applicability of our approach through validation on real fetal scan videos obtained from sonographers of varying expertise levels. Our findings demonstrate the potential of our approach to complement existing fetal US technologies and advance prenatal diagnostic practices.
Abstract (translated)
本文提出了一种新颖的超声平面姿态估计管道,旨在将超声平面姿态估计更有效地应用于胎儿脑中的标准平面(SPs),实现更有效的导航。我们提出了一种半监督分割模型,利用已标注的SPs和未标注的3D US volume切片。我们的模型在多样性的胎儿脑图像上实现可靠的分割。此外,模型还包括分类机制,可以精确地识别胎儿脑。我们的模型不仅滤除了缺乏脑的帧,还为包含脑的帧生成掩码,从而增强平面姿态回归在临床场景中的相关性。我们专注于从二维超声视频分析开始的胎儿脑导航,并将此模型与二维超声平面姿态回归网络相结合,提供无传感器距离检测到SPs和非SPs平面;我们强调对SPs进行距离检测对引导超声师的重要性,以及在扫描过程中更精确的调整的优势。我们通过验证来自不同专业水平的超声师获得的实际胎儿扫描视频来证明我们方法的实用性。我们的研究结果表明,我们的方法可以补充现有的胎儿US技术,促进产科诊断技术的进步。
URL
https://arxiv.org/abs/2404.07124
