Table 5.
Study | Objective | Backbone Methods/Framework | Optimization/Extractor methods | Fetal age | AI tasks |
---|---|---|---|---|---|
Fetal facial standard plane (FFSP) | |||||
(Lei et al., 2014) | To address the issue of recognition of standard planes (i.e., axial, coronal and sagittal planes) in the fetal US image | SVM classifier | AdaBoost for detect region of interest, ROI) Dense Scale Invariant Feature Transform (DSIFT) Aggregating vectors for feature extraction fish vector (FV) Gaussian Mixture Model (GMM) |
20 - 36 weeks | Classification |
(Yu et al., 2016) | To automatically recognize the FFSP from US images | Deep convolutional networks (DCNN) | N/A | 20 - 36 weeks | Classification |
(Yu et al., 2018) | To automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture | DCNN | t-Distributed Stochastic Neighbor Embedding (t-SNE) | 20 - 36 weeks | Classification |
(Lei et al., 2015) | To automatically recognize the fetal facial standard planes (FFSPs) | SVM classifier | Root scale invariant feature transform (RootSIFT) Gaussian mixture model (GMM) Fisher Vector (FV) Principal Component Analysis (PCA) |
20 - 36 weeks | Classification |
(Wang et al., 2021) | To automatically recognize and classify FFSPs | SVM classifier | Local Binary Pattern (LBP) Histogram of Oriented Gradient (HOG) |
20 - 24 weeks | Classification |
Face anatomical landmarks | |||||
(Singh et al., 2021a) | To detect position and orientation of facial region and landmarks | SFFD-Net (Samsung Fetal Face Detection Network) multi-class segmented | N/A | 14 - 30 weeks | Miscellaneous |
(Chen et al., 2020c) | To detect landmarks in 3D fetal facial US volumes | CNN Backbone Network | Region Proposal Network (RPN) Bounding-box regression |
N/A | Miscellaneous |
(Anjit and Rishidas, 2011) | To detect nasal bone for US of fetus | Back Propagation Neural Network (BPNN) | Discrete Cosine Transform (DCT) Daubechies D4 Wavelet transform |
11 - 13 weeks | Miscellaneous |
Facial expressions | |||||
(Dave and Nadiad, 2015) | To recognize facial expressions from 3D US | ANN | Histogram equalization Thresholding Morphing Sampling Clustering Local Binary Pattern (LBP) Minimum Redundancy and Maximum Relevance (MRMR) |
N/A | Classification |
(Miyagi et al., 2021) | To recognize fetal facial expressions that are considered as being related to the brain development of fetuses | CNN | N/A | 19 - 38 weeks | Classification |