FHUSP-NET: A Multi-task model for fetal heart ultrasound standard plane recognition and key anatomical structures detection

Abstract

In prenatal ultrasound screening, rapid and accurate recognition of the fetal heart ultrasound standard planes(FHUSPs) can more objectively predict fetal heart growth. However, the small size and movement of the fetal heart make this process more difficult. Therefore, we design a deep learning-based FHUSP recognition network (FHUSP-NET), which can automatically recognize the five FHUSPs and detect tiny key anatomical structures at the same time. 3360 ultrasound images of five FHUSPs from 1300 mid-pregnancy pregnant women are included in this study. 10 fetal heart key anatomical structures are manually annotated by experts. We apply spatial pyramid pooling with a fully connected spatial pyramid convolution module to capture information about targets and scenes of different sizes as well as improve the perceptual ability and feature representation of the model. Additionally, we adopt the squeeze-and-excitation networks to improve the sensitivity of the model to the channel features. We also introduce a new loss function, the efficient IOU loss, which makes the model effective for optimizing similarity. The results demonstrate the superiority of FHUSP-NET in detecting fetal heart key anatomical structures and recognizing FHUSPs. In the detection task, the value of mAP@0.5, precision, and recall are 0.955, 0.958, and 0.931, respectively, while the accuracy reaches 0.964 in the recognition task. Furthermore, it takes only 13.6 ms to detect and recognize one FHUSP image. This method helps to improve ultrasonographers' quality control of the fetal heart ultrasound standard plane and aids in the identification of fetal heart structures in a less experienced group of physicians.

Keywords: Deep learning; Fetal heart; Key anatomical structure detection; Standard plane recognition; Ultrasound images.