Segmentation of Five Components in Four Chamber View of Fetal Echocardiography

It is clinically significant to segment five components in four chamber view of fetal echocardiography, including four chambers and the descending aorta. This study completes the multi-disease segmentation and multi-class semantic segmentation of the five key components. After comparing the performance of DeeplabV3+ and U-net in the segmentation task, we choose the former as it provides accurate segmentation in other six disease groups as well as the normal group. With the data proportion balance strategy, the segmentation performance of the Ebstein's anomaly group is improved significantly in spite of its small proportion. We empirically evaluate this strategy in terms of mean iou (MIOU), cross entropy loss (CE) and dice score (DS). The proportion of the atrial abnormality and ventricular abnormality in the entire data set is increased, so that the model learns more semantics. We simulate multiple scenes with uncertain attitudes of the fetus, which provides rich multi-scene semantic information and enhances the robustness of the model.