Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Table 1 Different applications for artificial intelligence for fetal brain magnetic resonance imaging

Classification		Different applications
A	AI for preprocessing of fetal images	(1) Automatic image quality assessment to detect artifacts on T2 HASTE sequences during fetal MRI (Gagoski et al[9]); (2) Automatically detects fetal landmarks (using 15 key points–upper limb and lower limb joints, eyes, and bladder) (Xu et al[10]); (3) Fetal motion correction (Hou et al[11]); and (4) Predicting fetal motion directly from acquired images in real-time (Singh et al[12])
B	AI for post-processing of fetal images	(1) U-net-based brain extraction algorithm to autonomously segment normal fetal brains (Li et al[14]); and (2) Localize, segment, and perform super-resolution reconstruction for the automated fetal brain (Ebner et al[15])
C	AI for reconstruction of fetal imaging	Fully automatic framework for fetal brain reconstruction, consisting of four stages (Ebner et al[15])
D	AI for gestational age prediction	(1) Predicting GA from fetal brain MRI acquired after the first trimester, which was compared to a BPD (Kojita et al[19]); and (2) An end-to-end, attention-guided deep learning model that predicts GA (Shen et al[20])
E	AI for fetal brain extraction	(1) The automatic brain extraction method for fetal MRI employs a multi-stage 2D U-Net with deep supervision (DS U-net) (Lou et al[24]); and (2) A brain mask for an MRI stack using a two-phase random forest classifier and one estimated high-order Markov random field solution (Ison et al[23])
F	AI for fetal brain segmentation	(1) U-net-like convolutional neural network (Auto-net) (Mohseni Salehi et al[29]); CNN using images with synthetically induced intensity inhomogeneity as data augmentation (Mohseni Salehi et al[29]); (2) Pipeline for performing ICV localization, ICV segmentation, and super-resolution reconstruction in fetal MR data in a sequential manner (Tourbier et al[32]); (3) Automatic method for fetal brain segmentation from MRI data, and a normal volumetric growth chart based on a large cohort (Link et al[33]); (4) Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain and its class labels (de Dumast et al[34]); (5) SIMOU-Net, a hybrid network for fetal brain segmentation. Was inspired by the original U-Net fused with the HED network (Rampun et al[36]); and (6) Incorporating spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework (Long et al[35])
G	AI for fetal brain linear measurement	(1) AI for the anteroposterior (A/P) diameter of the pons and the A/P diameter and S/I height of the vermis (Deng et al[40]); and (2) A fully automatic method that computes three key fetal brain MRI parameters: 1-CBD, 2-BBD, 3-TCD (Avisdris et al[41])
H	AI for automatically localizing fetal anatomy	Automatically localizing fetal anatomy, notably the brain, using extracted superpixels (Alansary et al[42])
I	AI for classification of brain pathology	Classification using several machine-learning classifiers, including DQDA, K-NN, random forest, naive Bayes, and RBF neural network classifiers (Attallah et al[45])
J	AI for placenta detection	(1) U-net-based CNN to separate the uterus and placenta (Shahedi et al[51]); and (2) automatic placenta segmentation by deep learning on different MRI sequences (Specktor-Fadida et al[52])
K	AI for functional fetal brain MRI	An auto-masking model with fMRI preprocessing stages from existing software (Rutherford et al[53])

AI: Artificial intelligence; MRI: Magnetic resonance imaging; RBF: Radial basis function; K-NN: K-nearest neighbor; DQDA: Diagonal quadratic discriminant analysis; CBD: Cerebral Biparietal Diameter; BBD: Bone Biparietal Diameter; TCD: Trans Cerebellum Diameter; S/I: Superior/inferior; SIMOU-Net: Single-Input Multi-Output U-Net; HED: Holistically nested edge detection; BPD: Biparietal diameter; ICV: Intracranial volume.