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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Magn Reson Imaging. 2019 Jul 5;52(4):998–1018. doi: 10.1002/jmri.26852

TABLE 2.

Review of Machine-Learning Studies Evaluating Lesion Segmentation

Study N= Lesion type Initial segmentation training Machine-learning method for segmentation Segmentation similarity metrics
Dalm1ș 2018 385 Mass, NME Manual annotations 3-D CNN CPM = 0.6429
Gallego-Ortiz 2018 792 NME 2D bounding box ANN NS
Herent 2019 50* NS 2D bounding box CNN (ResNet-50) NS
Zhou 2019 1537 Mass Manual annotations; image-level classification method CNN (classification activation map + DenseNet DSC 0.501
*

Evaluated only a single slice from each breast MRI; other studies listed evaluated the lesion using multiple slices.

NS = not specified; NME = nonmass enhancement; CNN = convolutional neural network; ANN = artificial neural network; CPM = computation performance metric; DSC = Dice similarity coefficient.