Table 4 Comparison of the existing state-of-the-arts on MICCAI MSSEG 2016 Training Data65,68. (TPR = TPF; PPV = 1 − FPF).

From: Radius-optimized efficient template matching for lesion detection from brain images

Group

Method

MRI sequence

Method type

Performance

DICE

TPR

PPV

Mahbod et al.59

Multilayer perceptron with morphology-based filtering

3D FLAIR

Sup. Seg.

10.2–84.0

–

–

Vera-Olmos et al.60

RF classifier and MRF based post-processing

T1, FLAIR

Sup. Seg.

63.8

68.3

–

Salehi et al.61

3D Fully convolutional network with Tversky loss

T1, T2, FLAIR

Sup. Seg.

56.4

56.8

–

Hashemi et al.62

Patch-wise 3D fully convolutional DenseNet architecture

T1, T1-GADO, FLAIR, PD, T2

Sup. Seg.

69.9

78.5

–

Coupe et al.63

Rotationally-invariant NLM and patch-wise NLM denoising filter

T1, FLAIR

Sup. Seg.

72.5

–

–

Chen et al.64

Hybrid feature network based on DenseNet architecture

T1, T2, FLAIR

Sup. Seg.

66.5

61.3

–

Kamraoui et al.65

DeepLesionBrain (DLB)

T1, FLAIR

Sup. Seg.

63.9

60.8

76.8

DLB with hierarchical specialization learning

T1, FLAIR

66.9

67.1

72.8

Valverde et al.66

3D cascaded CNN with

\(11\times 11\times 11\) patch

T1, FLAIR

Sup. Seg.

44.2

42.3

61.4

Zhang et al.67

2.5D densely connected fully convolutional network

T1, FLAIR

Sup. Seg.

66.4

65.8

74.1

McKinley et al.68

3D-2D CNN (DeepSCAN) architecture

T1, T2, FLAIR

Sup. Seg.

75.7

–

–

Valverde et al.44

Cascaded 3D CNN

T1, T2, FLAIR

Sup. Seg.

58.7

–

–

Isensee et al.69

2D, 3D, cascade of two 3D U-Net

T1, T2, FLAIR

Sup. Seg.

74.5

–

–

Beaumont et al.58

Multimodal graph cut, EM, and post-processing

T1, T2, FLAIR

UnSup. Seg.

57.0

–

–

Beaumont et al.70

Voxel-wise comparison (GMM and EM) and post-processing

FLAIR

UnSup. Seg.

50.5

–

–

T2 \(\cup\) FLAIR

42.4

–

–

T2 \(\cap\) FLAIR

43.9

–

–

FLAIR and T2 \(\cap\) FLAIR

56.6

–

–

Beaumont et al.70

Voxel-wise comparison without post-processing

FLAIR

UnSup. Seg.

42.3

–

–

T2 \(\cup\) FLAIR

29.7

–

–

T2 \(\cap\) FLAIR

24.7

–

–

Knight and Khademi71

Fuzzy classification, thresholding, post-processing

FLAIR

UnSup. Seg.

60.0

53.0

80.0

Baseline method

FFT-based template matching in \(\mathcal{O}\left({a}_{max}N\mathrm{log}N\right)\)

FLAIR

UnSup. Detection

11.0

20.0

11.8

8.8

40.0

6.1

6.3

60.0

3.8

4.4

80.0

2.4

Proposed

Template matching in \(\mathcal{O}\left({a}_{max}N\right)\)

FLAIR

UnSup. Detection

11.2

20.0

12.2

9.5

40.0

6.8

6.7

60.0

4.0

4.6

80.0

2.5

Proposed

Template matching in \(\mathcal{O}\left(N\right)\)

FLAIR

UnSup. Detection

13.5

20.0

20.2

12.0

40.0

9.6

8.5

60.0

5.4

6.2

80.0

3.5