Abstract: Abstract： The shape and size of the placenta in early pregnancy are closely related to clinical outcomes such as fetal growth. Aiming at the time-consuming interactive segmentation method for three-dimensional ultrasound （3DUS） detection of placental size， a new deep learning segmentation network， DEC-U-Net， is designed based on the U-Net architecture. In the U-Net downsampling stage， deep hyperparametric convolution is used instead of 2D convolution and combined with the ECA attention mechanism. However， the accuracy of placenta detailed feature recognition is improved while introducing more parameter quantities. The cross attention mechanism is introduced into jump linking to solve the problems of blurred placental boundaries and uneven contrast. Compared with ordinary U-Net networks， the algorithm in this paper improves the intersection and merge ratio （IoU）， recall rate （Recall）， accuracy （Precision）， and Dice coefficient by 4.14， 9.59， 6.2， and 16.41 percentage points， respectively. The experimental results show that the improved network model has a good segmentation effect and can accurately segment the placenta in ultrasound images.

Key words: Key words： fetal ultrasound images, placental testing, Do-Conv, ECA attention, MHCA