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Table 1 Imaging system and typical parameters

From: Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network

MR Vendor Algorithm Development# (N = 264) External Set* (N = 60) Typical parameters (DWI sequence)
Training set (N = 208) Validation set (N = 28) Testing set (N = 28)
3.0 T Discovery (Ge healthcare, Milwaukee, WI) N = 157 N = 20 N = 20 N = 41 B value: 0, 800 s/mm2;
Imaging matrix:256 × 256;
Echo time: 60 ms;
Repetition time: 3000 ms;
Field of view: 360 mm;
Section thickness: 4 mm
Number of slices: 25
3.0 T Intera (Philips Medical Systems, Best, the Netherlands) N = 37 N = 5 N = 5 N = 11 B value: 0, 1000 s/mm2;
Imaging matrix: 240 × 240;
Echo time:78 ms;
Repetition time: 4959 ms;
Field of view: 360 mm;
Section thickness:7 mm
Number of slices: 28
1.5 T Avanto (Siemens Medical Solutions, Erlangen, Germany) N = 14 N = 3 N = 3 N = 8 B value: 0, 800 s/mm2;
Imaging matrix: 156 × 180;
Echo time:54 ms;
Repetition time: 3300 ms;
Field of view: 360 mm;
Section thickness: 7 mm
Number of slices: 24
  1. #The data in algorithm development were collected between August 2018 and August 2019
  2. *The data in external set were collected between January 2020 and March 2020