Automatic measurement of fetal abdomen subcutaneous soft tissue thickness from ultrasound image based on a U-shaped attention network with morphological method

Fetal abdominal subcutaneous soft tissue thickness (FASSTT) is a key indicator in evaluating fetal growth, development, and nutritional status. Currently, manual measurement in FASSTT faces the problems of difficulty in positioning, time consumption, and inaccurate measurement. Therefore, this article proposes an automatic measurement scheme for FASSTT. Firstly, a U-shaped attention network VGG-SeUnet is proposed to automatically segment the subcutaneous soft tissue area of the fetal abdomen. Secondly, based on the segmentation results, a morphological method is proposed to obtain FASSTT. Specifically, the segmentation network uses VGG16 as the encoder and connects the decoder through jump connections for multi-scale fusion. We introduce channel attention to jump connections, which enables the model to select key channels for feature fusion. At the same time, the model uses the proposed DF_Loss function for training to solve the problem of sample imbalance. Based on the segmentation results, a morphological distance transformation algorithm is proposed to obtain FASSTT by drawing the maximum inscribed circle and calculating its diameter. The method is evaluated on a fetal abdominal circumference ultrasound dataset containing 135 samples. The DSC, mIOU, mPA, Precision and Recall of the segmentation experiments reache 88.9%, 81.02%, 90.44%, 86.38%, and 90.44% respectively. The difference between FASSTT's result from that provided by radiologist is 0.0615 cm in Average Error (AVE) and 0.081 cm in Root Mean Square Error (RMSE). The overall performance of this algorithm is superior to existing methods with excellent performance, and the measurement error is less than 1 millimeter. This result proves that the proposed scheme in this paper can achieve automated measurement of FASSTT and assist doctors in subsequent evaluation of fetal development and nutritional status.