Danish Hafiz Muhammad, Suhail Zobia, Farooq Faiza
Department of Computer Science, University of the Punjab, Lahore, Pakistan.
Department of Radiology, University of Lahore Teaching Hospital, Lahore, Pakistan.
Front Pediatr. 2024 Dec 18;12:1453302. doi: 10.3389/fped.2024.1453302. eCollection 2024.
Monitoring the morphological features of the gestational sac (GS) and measuring the mean sac diameter (MSD) during early pregnancy are essential for predicting spontaneous miscarriage and estimating gestational age (GA). However, the manual process is labor-intensive and highly dependent on the sonographer's expertise. This study aims to develop an automated pipeline to assist sonographers in accurately segmenting the GS and estimating GA.
A novel dataset of 500 ultrasound (US) scans, taken between 4 and 10 weeks of gestation, was prepared. Four widely used fully convolutional neural networks: UNet, UNet++, DeepLabV3, and ResUNet were modified by replacing their encoders with a pre-trained ResNet50. These models were trained and evaluated using 5-fold cross-validation to identify the optimal approach for GS segmentation. Subsequently, novel biometry was introduced to assess GA automatically, and the system's performance was compared with that of sonographers.
The ResUNet model demonstrated the best performance among the tested architectures, achieving mean Intersection over Union (IoU), Dice, Recall, and Precision values of 0.946, 0.978, 0.987, and 0.958, respectively. The discrepancy between the GA estimations provided by the sonographers and the biometry algorithm was measured at a Mean Absolute Error (MAE) of 0.07 weeks.
The proposed pipeline offers a precise and reliable alternative to conventional manual measurements for GS segmentation and GA estimation. Furthermore, its potential extends to segmenting and measuring other fetal components in future studies.
监测妊娠囊（GS）的形态特征并在妊娠早期测量平均囊径（MSD）对于预测自然流产和估计胎龄（GA）至关重要。然而，人工操作过程劳动强度大且高度依赖超声检查人员的专业知识。本研究旨在开发一种自动化流程，以协助超声检查人员准确分割妊娠囊并估计胎龄。
准备了一个包含500例妊娠4至10周超声（US）扫描的新数据集。通过用预训练的ResNet50替换其编码器，对四个广泛使用的全卷积神经网络：UNet、UNet++、DeepLabV3和ResUNet进行了修改。这些模型使用5折交叉验证进行训练和评估，以确定妊娠囊分割的最佳方法。随后，引入了新的生物测量方法来自动评估胎龄，并将该系统的性能与超声检查人员的性能进行比较。
ResUNet模型在测试的架构中表现最佳，平均交并比（IoU）、Dice系数、召回率和精确率分别达到0.946、0.978、0.987和0.958。超声检查人员提供的胎龄估计值与生物测量算法之间的差异以平均绝对误差（MAE）0.07周来衡量。
所提出的流程为妊娠囊分割和胎龄估计提供了一种精确且可靠的替代传统手动测量的方法。此外，其潜力在未来研究中扩展到分割和测量其他胎儿成分。
