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EvaluateSegmentation Tool, VISCERAL http://www.visceral.eu
VISCERAL received funding from EU FP7, contract 318068
Developed by Abdel Aziz Taha ([email protected])
24.02.2013
Copyright 2013 Vienna University of Technology I nstitute of Software Technology and Interactive Systems

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

1 Description
EvaluateSegmentation is a tool that compares two volumes (a test segmentation and a ground truth segmentation) using 22 different metrics that were selected as a result of a comprehensive research into the metrics used in the medical volume segmentations. EvaluateSegmentation provides the following measures:

Similarity

  1. Dice Coefficient
  2. Jaccard Coefficient
  3. Area under ROC Curve (one system state)
  4. Cohen Kappa
  5. Rand Index
  6. Adjusted Rand Index
  7. Interclass Correlation
  8. Volumetric Similarity Coefficient
  9. Mutual Information

Distance
10. Hausdorff Distance
11. Average Distance
12. Mahanabolis Distance
13. Variation of Information
14. Global Consistency Error
15. Coefficient of Variation
16. Probabilistic Distance

Classic measures
17. Sensitivity (Recall, true positive rate)
18. Specificity (true negative rate)
19. Precision
20. F-Measure
21. Accuracy
22. Fallout (false positive rate)

2 Supported Images

EvaluateSegmentation is built on the ITK Library thus it supports all 3D file formats
that are supported by ITK, e.g .nii, .mha, etc. The two Images should however have the
same size (the same number of grid size). There should be only one label in an image,
where a voxel value can be either zero (background) or a value between zero and
one [0,1] that denotes the fuzzy membership or the probability that the
corresponding voxel belongs to the label.

3 Syntax

  1. Evaluation of volume segmentations:

USAGE:

EvaluateSegmentation truthPath segmentPath [-thd threshold] [-xml xmlpath] [-use all|DICE,JACRD, ....]

where:
truthPath: path (or URL) to truth image. URLs should be enclosed with quotations.
segmentPath: path (or URL) to image being evaluated. URLs should be enclosed with quotations.
-th threshold: before evaluation convert fuzzy images to binary using the given threshold.
-xml xmlpath: path to xml file where results should be saved.
-help: more information
-use metriclist:this option can be used to specify which metrics should be used.

Metriclist consists of the codes of the desired metrics separated by commas. For those metrics that accept parameters, it is possible to pass these parameters by writing them between two @ characters, e.g. –use MUTINF,[email protected]@. This option tells the tool to calculate the mutual information and the F-Measure at beta=0.5.

Possible codes for metriclist are:

	all: calculate all available metrics (default)  
	DICE: calculate Dice Coefficient  
	JACRD: calculate Jaccard Coefficient  
	GCOERR: calculate Global Consistency Error  
	VOLSMTY: calculate Volumetric Similarity Coefficient  
	KAPPA: calculate Cohen Kappa  
	AUC: calculate Area under ROC Curve (one system state)  
	RNDIND: calculate Rand Index  
	ADJRIND: calculate Adjusted Rand Index  
	ICCORR: calculate Interclass Correlation   
	MUTINF: calculate Mutual Information  
	FALLOUT: calculate Fallout (false positive rate)  
	COEFVAR: calculate Coefficient of Variation  
	AVGDIST: calculate Average Distance  
	HDRFDST: calculate Hausdorff Distance [email protected]@ means use 0.95 quantile to avoid outliers. Default is quantile of 1 which  		means exact Hausdorff distance  
	VARINFO: calculate Variation of Information  
	PROBDST: calculate Probabilistic Distance  
	MAHLNBS: calculate Mahanabolis Distance  
	SNSVTY: calculate Sensitivity (Recall, true positive rate)  
	SPCFTY: calculate Specificity (true negative rate)  
	PRCISON: calculate Precision  
	FMEASR: calculate F-Measure  [email protected]@ means use 0.5 as a  
	value for beta in the F-Measure  
	ACURCY: calculate Accuracy

Examples
Example 1: EvaluateSegmentation truth.nii segment.nii –use RNDIND,[email protected]@, [email protected]@ -xml result.xml

This example shows how to compare two NIFTI images providing the Rand Index, Hausdorff distance and the F-Measure and save the results in result.xml. The values between two @ symbols are parameters to the specific measures in this case the quantile value used with Hausdorff distance to avoid outliers. The second value (0.5) is the beta value used with the F-Measure.

Example 2: EvaluateSegmentation truth.nii segment.nii –use all -th 0.5

This example compares two images using all available metrics. Before comparing the images, they are converted to binary images using a threshold of 0.5, that is voxels with values in [0,0.5) are considered as background and those with values in [0.5,1] are assigned the label with a membership of 1.

  1. Evaluation of landmark localization:

USAGE:
EvaluateSegmentation -loc truth_landmark_path test_landmark_path [-xml output_xml_path]

where:
truth_landmark_path =path (or URL) to file containing ground truth landmarks. URLs should be enclosed with quotations.

test_landmark_path =path (or URL) to file containing landmarks to be evaluated. URLs should be enclosed with quotations.

-xml =path to xml file where result should be saved.