Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part IV
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part IV
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医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第四部分
DOI：
10.1007/978-3-031-16440-8_27
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发表时间：
2022
期刊：
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通讯作者：
Avisdris N
中科院分区：
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作者：
Avisdris N
来自超声的胎儿生长评估是基于一些生物测量，这些测量是手动进行的，并相对于预期的胎龄进行评估。可靠的生物统计学估计依赖于标准超声平面中界标的精确检测。手动注释可能是耗时且依赖于操作员的任务，并且可能导致高测量变异性。用于自动胎儿生物测定的现有方法依赖于初始自动胎儿结构分割，随后是几何标志检测。然而，分割注释是耗时的，并且可能是不准确的，并且地标检测需要开发测量特定的几何方法。本文介绍了BiometryNet，这是一个用于胎儿生物统计估计的端到端地标回归框架，它克服了这些限制。它包括一种新的动态方向确定（DOD）方法，用于在网络训练期间执行特定于测量的方向一致性。DOD减少了网络训练中的变异性，提高了地标定位精度，从而产生准确和鲁棒的生物特征测量。为了验证我们的方法，我们收集了来自1，829名受试者的3，398张超声图像的数据集，这些受试者在三个临床站点使用七种不同的超声设备采集。在两个独立的数据集上对三种不同的生物特征测量进行比较和交叉验证表明，BiometryNet具有鲁棒性，并且可以产生准确的测量结果，其误差低于临床允许的误差，优于其他现有的自动生物特征估计方法。代码可在https://github.com/netanellavisdris/fetalbiometry上获得。
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inaccurate, and landmark detection requires developing measurement-specific geometric methods. This paper describes BiometryNet, an end-to-end landmark regression framework for fetal biometry estimation that overcomes these limitations. It includes a novel Dynamic Orientation Determination (DOD) method for enforcing measurement-specific orientation consistency during network training. DOD reduces variabilities in network training, increases landmark localization accuracy, thus yields accurate and robust biometric measurements. To validate our method, we assembled a dataset of 3,398 ultrasound images from 1,829 subjects acquired in three clinical sites with seven different ultrasound devices. Comparison and cross-validation of three different biometric measurements on two independent datasets shows that BiometryNet is robust and yields accurate measurements whose errors are lower than the clinically permissible errors, outperforming other existing automated biometry estimation methods. Code is available at https://github.com/netanellavisdris/fetalbiometry.