Department of Gynaecology and Obstetrics, the First Affiliated Hospital of Jinan University, Guangzhou, China.
Anesthesiology department, the First Affiliated Hospital of Jinan University, Guangzhou, China.
Math Biosci Eng. 2021 Sep 9;18(6):7790-7805. doi: 10.3934/mbe.2021387.
The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model from the two aspects of accuracy and robustness. In this study, we proposed a traditional machine learning method to detect the endpoints of FL based on a random forest regression model. Deep learning methods based on SegNet were proposed for the automatic measurement method of FL, which utilized skeletonization processing and improvement of the full convolution network. Then the automatic measurement results of the two methods were evaluated quantitatively and qualitatively with the results marked by doctors. 436 ultrasonic fetal femur images were evaluated by the two methods above. Compared the results of the above three methods with doctor's manual annotations, the automatic measurement method of femur length based on the random forest regression model was 1.23 ± 4.66 mm and the method based on SegNet was 0.46 ± 2.82 mm. The indicator for evaluating distance was significantly lower than the previous literature. Measurement method based SegNet performed better in the case of femoral end adhesion, low contrast, and noise interference similar to the shape of the femur. The segNet-based method achieves promising performance compared with the random forest regression model, which can improve the examination accuracy and robustness of the measurement of fetal femur length in ultrasound images.
本工作旨在初步临床验证和评估我们的自动算法在评估超声图像中胎儿股骨长度（FL）进展方面的准确性。从准确性和鲁棒性两个方面比较随机森林回归模型和 SegNet 模型。在这项研究中，我们提出了一种基于随机森林回归模型的传统机器学习方法来检测 FL 的端点。提出了基于 SegNet 的深度学习方法用于 FL 的自动测量方法，该方法利用了骨骼化处理和全卷积网络的改进。然后，用医生标记的结果对这两种方法的自动测量结果进行定量和定性评估。对 436 张超声胎儿股骨图像进行了上述两种方法的评估。将上述三种方法的结果与医生的手动标注进行比较，基于随机森林回归模型的股骨长度自动测量方法为 1.23±4.66mm，基于 SegNet 的方法为 0.46±2.82mm。评估距离的指标明显低于之前的文献。在股骨末端粘连、对比度低以及与股骨形状相似的噪声干扰情况下，基于 SegNet 的方法表现更好。与随机森林回归模型相比，基于 SegNet 的方法具有更好的性能，可以提高超声图像中胎儿股骨长度测量的检查准确性和鲁棒性。
