An improved semantic segmentation with region proposal network for cardiac defect interpretation

Detecting cardiac abnormalities between 14 and 28 weeks of gestation with an apical four-chamber view is a difficult undertaking. Several unfavorable factors can prevent such detection, such as the fetal heart's relatively small size, unclear appearances in anatomical structures (e.g., shadows), and incomplete tissue boundaries. Cardiac defects without segmentation are not always straightforward to detect, so using only segmentation cannot produce defect interpretation. This paper proposes an improved semantic segmentation approach that uses a region proposal network for septal defect detection and combines two processes: contour segmentation with U-Net architecture and defect detection with Faster-RCNN architecture. The model is trained using 764 ultrasound images that include three abnormal conditions (i.e., atrial septal defect, ventricular septal defect, and atrioventricular septal defect) and normal conditions from an apical four-chamber view. The proposed model produces a satisfactory mean intersection over union, mean average precision, and dice similarity component metrics of about 75%, 87.80%, and 96.37%, respectively. Furthermore, the proposed model has also been validated on 71 unseen images in normal conditions and produces 100% sensitivity, which means that all normal conditions without septal defects can be detected effectively. The developed model has the potential to identify the fetal heart in normal and pathological settings accurately. The developed deep learning model's practical use in identifying congenital heart disorders has substantial future promise.