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Table 1 Overview of the metrics implemented in this tool

From: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Metric

Symb.

Reference of use in medical images

cat.

Definition

Dice (=F1-Measure)

DICE

[1, 2, 15, 16, 5763]

1

(6)

Jaccard index

JAC

[15, 16, 2123, 59, 60, 62]

1

(7)

True positive rate (Sensitivity, Recall)

TPR

[10, 16, 60, 6264]

1

(10)

True negative rate (Specificity)

TNR

[10, 16, 60, 62]

1

(11)

False positive rate (=1-Specificity, Fallout)

FPR

→ Specificity

1

(12)

False negative rate (=1-Sensitivity)

FNR

→ Sensitivity

1

(13)

F-Measure (F1-Measure=Dice)

FMS

→ Dice

1

(15), (16)

Global Consistency Error

GCE

[2123, 65, 66]

1

(17) to (19)

Volumetric Similarity

VS

[15, 2123, 59, 61, 67]

2

(21)

Rand Index

RI

[21, 22, 65, 66]

3

(30)

Adjusted Rand Index

ARI

[68, 69]

3

(32)

Mutual Information

MI

[2, 32, 57]

4

(33) to (38)

Variation of Information

VOI

[21, 22, 65, 66]

4

(39), (35)

Interclass correlation

ICC

[8, 70]

5

(41)

Probabilistic Distance

PBD

[8, 59]

5

(43)

Cohens kappa

KAP

[1, 62]

5

(44) to (46)

Area under ROC curve

AUC

[2, 64, 69]

5

(47)

Hausdorff distance

HD

[8, 15, 59, 6163, 71, 72]

6

(48), (49)

Average distance

AVD

[62, 63]

6

(50), (51)

Mahalanobis Distance

MHD

[15, 73]

6

(52) to (54)

  1. The symbols in the second column are used to denote the metrics throughout the paper. The column “reference of use” shows papers where the corresponding metric has been used in the evaluation of medical volume segmentation. The column “category” assigns each metric to one of the following categories: (1) Overlap based, (2) Volume based, (3) Pair counting based, (4) Information theoretic based, (5) Probabilistic based, and (6) Spatial distance based. The column “definition” shows the equation numbers where the metric is defined