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Table 5 The comparison of liver segmentation methods

From: An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

Authors

DSC

Number of data

Run time

López-Mir et al.[22]

0.95

16 exams

59–120 slices/exam

7 s/image

(Intel Corei5 2.80 GHz CPU, 2 GB RAM)

Bereciartua et al. [24]

0.90

18 exams

21 slices/exam

0.53 s/image

(Intel core2quad 3.00 GHz CPU, N/A RAM)

Göçeri [63]

0.95

10 slices

16.80 s/image

(Intel

Pentium 2.40 GHz CPU, 2 GB RAM)

Shen et al. [62]

0.94

40 exams

44 slices/exam

20 min/case

(Intel Corei5 1.3 GHz CPU, 8 GB RAM)

Huynh et al. [61]

0.91

27 exams

44–120 slices/exam

8.4 min/case

(Intel Xeon 2.66 GHz CPU, N/A RAM)

Wang et al. [38]

0.93 (T2*w)

0.95 (T1w)

168 exams (T2*w)

6 slices (TEs)/exam

100 exams (T1w)

N/A

Jansen et al. [64]

0.95

55 exams

100 slices/exam

33 exams for training

3 exams for validation

19 exams for testing

N/A

Proposed method

0.93

537 exams

20 slices (TEs)/exam

441 exams for training

112 exams for testing

0.31 s/image

(Intel Corei7 4.30 GHz, 16 GB RAM)