Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal
  Biometric Measurements
Shuge Lei
Medical image segmentation, particularly in the context of ultrasound data,
is a crucial aspect of computer vision and medical imaging. This paper delves
into the complexities of uncertainty in the segmentation process, focusing on
fetal head and femur ultrasound images. The proposed methodology involves
extracting target contours and exploring techniques for precise parameter
measurement. Uncertainty modeling methods are employed to enhance the training
and testing processes of the segmentation network. The study reveals that the
average absolute error in fetal head circumference measurement is 8.0833mm,
with a relative error of 4.7347%. Similarly, the average absolute error in
fetal femur measurement is 2.6163mm, with a relative error of 6.3336%.
Uncertainty modeling experiments employing Test-Time Augmentation (TTA)
demonstrate effective interpretability of data uncertainty on both datasets.
This suggests that incorporating data uncertainty based on the TTA method can
support clinical practitioners in making informed decisions and obtaining more
reliable measurement results in practical clinical applications. The paper
contributes to the advancement of ultrasound image segmentation, addressing
critical challenges and improving the reliability of biometric measurements.