Open AccessSegmentation of ten fetal heart components with coarse‐to‐fine cascading and dynamic feature powering
Author(s) -
Yang Tingyang,
Zhang Ye,
Zhu Mengxiao,
Wang Yan,
An Shan,
Gu Xiaoyan,
Liu Xiaowei,
Han Jiancheng,
He Yihua,
Zhu Haogang
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12597
Subject(s) - feature (linguistics) , segmentation , artificial intelligence , pattern recognition (psychology) , pixel , intersection (aeronautics) , computer science , orientation (vector space) , feature extraction , computer vision , position (finance) , extractor , image segmentation , mathematics , engineering , geometry , philosophy , linguistics , finance , process engineering , economics , aerospace engineering
Segmenting heart components in the apical four‐chamber view of fetal echocardiography is of critical significance in clinical practice. However, it is difficult to recognize these components due to small‐scale components and the imbalanced ventricular apex orientation. In this study, a novel segmentation framework is proposed to segment ten general fetal heart components for the first time. This framework consists of a multi‐directional fine‐density (MDFD) data augmentation method and a coarse‐to‐fine cascade network (CFCN). MDFD enhances the apex orientation diversity and balances the orientation distribution. CFCN has two stages including a coarse network and a fine network. These two stages have similar structures that consist of a feature extractor and a feature refined layer named as Element‐Wise Power with Dynamic Exponent layer (EWPDE). EWPDE which is a plug‐and‐play module for segmentation refines the features from the feature extractor to position small components accurately. By adopting EWPDE, the influence of each pixel is adjusted and hard pixels of small components are segmented precisely. Based on the dataset, the method is proved to be effective with the high mean intersection over union (mIoU) value and low missing ratio (MR). With MDFD and EWPDE, CFCN that adopts DeepLabV3+ as the feature extractor outperforms the best segmentation results (mIoU:0.480, MR:0.035). Compared to the original performance (mIoU:0.407, MR:0.085) of DeepLabV3+, the method improves the results significantly.