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.

翻译：本文提出一种新型技术流程，旨在将超声切面位姿估计更贴近临床实际，以实现对胎儿脑部标准切面（SPs）的有效导航。我们设计了一种半监督分割模型，同时利用标注的标准切面和未标注的三维超声体积切片，该模型能在多样的胎儿脑部图像中实现可靠分割。此外，模型集成了分类机制以精确识别胎儿脑部结构——不仅能滤除不含脑部的图像帧，还能为包含脑部的图像生成掩膜，从而增强临床场景中切面位姿回归的相关性。研究聚焦于二维超声视频分析中的胎儿脑部导航，将该模型与超声切面位姿回归网络相结合，实现对标准切面和非标准切面的无传感器邻近度检测。我们强调标准切面邻近度检测对引导超声医师的重要性，与常规方法相比，该方法能实现更早、更精确的扫描调整。通过在具有不同经验水平超声医师采集的真实胎儿扫描视频上进行验证，我们展示了该方法的实际适用性。研究结果表明，我们的方法具有补充现有胎儿超声技术并推动产前诊断实践的潜力。