[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

Table 1

Overview of reviewed literatures.

Authors	Reference	Year	Article title	Proposed approach	Type of images	Training images	Testing images	Limitation
Singh et al.	[18]	2021	2D fetal US brain images cerebellum semantic segmentation	ResU-Net-c	US	588	146	The model does not segment the cerebellum automatically
Zhao et al.	[19]	2022	Optimized DL method-based automated segmentation of 3D fetal brain images	3D U-Net	MR	65	41	Not all the evaluation metrics are computed
Hesse et al.	[20]	2022	Fetal brain’s subcortical segmentation in 3D US images by DL methods	CNN	US	215	20	Segmentation robustness requires more improvement
Fidon et al.	[21]	2021	DRO-based abnormal brain of fetal segmentation using 3D MR images	nnU-Net-DRO	MR	116	26	The process of segmentation may take more time to complete
Kim et al.	[22]	2019	Fetal-head biometry automatic evaluation by ML	DL-based method	US	102	70	The method is employed only on smaller datasets
Khalili et al.	[23]	2019	Automatic segmentation of fetal brain tissue by CNN	CNN	MR	32	94	The error rate of developed method was not measured
Avisdris et al.	[24]	2021	Fetal brain automated linear measures by DNN	DNN	MR	121	33	The linear measurements take more time
Dumast et al.	[25]	2022	Segmentation of fetal brain tissue from synthetic MR images	FaBiaN	MR	17	11	The developed model is not applicable for large datasets
Rackerseder et al.	[26]	2019	3D brain US fully automatic segmentation	DeepVNet	US	N/A	N/A	The quantitative evaluation takes more time
Venturini et al.	[27]	2019	Structural semantic segmentation using multitask CNN from 3D US images	Multitask CNN	US	480	48	The presented approach is challenging