TABLE 1.

Review of Machine-Learning Breast Anatomic Segmentation Techniques

Study	N=	Sequences	Segmentation	Deep-learning method	Similarity metrics
Wang 2013	4	T1, T2, PD, Dixon	FGT	Hierarchical SVM	1. Overlap ratios93.25–94.08%
Dalm1ș 2017	66	Pre T1W WOFS	Anatomic, FGT	1. 2C U-Net x 2 2. 3C U-Net	1. FGT DSC = 0.811 2. FGT DSC = 0.850
Xu 2018	50	Pre, post T1W	Anatomic	2D U-Net	DSC 0.9744
Fashandi 2019	85	Pre T1W WOFS, FS T1W	Anatomic	1. 2CU-Net with WOFS, FS, mixed, multi-channel input 2. 3C U-Net, same inputs	1. 2C U-net NonFS DSC = 0.96 2. 3D multi-channel DSC = 0.96
Ha 2019	137	Pre, post, sub	FGT, BPE	Modified 3D CNN/2D U-Net	1. FGT DSC 0.813 2. BPE DSC 0.829
Ivanovska 2019	40	Pre T1W WOFS	Anatomic, FGT	2C 2D U-Net x 2	3. Anat DSC 0.98 4. FGT DSC 0.932
Zhang 2019	114	Pre T1W WOFS	Anatomic, FGT	U-Net x 2	1. Anat DSC 0.86 2. FGT DSC 0.83

FS = fat saturated; WOFS = without fat saturation; ₊FGT = fibroglandular tissue; BPE = background parenchymal enhancement; Anat = anatomic segmentation (breast/chest wall/air).