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

Overview of attention for article published in BMC Medical Imaging, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#6 of 665)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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1 news outlet
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4 X users
patent
4 patents
wikipedia
5 Wikipedia pages

Citations

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1588 Dimensions

Readers on

mendeley
1513 Mendeley
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Title
Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
Published in
BMC Medical Imaging, August 2015
DOI 10.1186/s12880-015-0068-x
Pubmed ID
Authors

Abdel Aziz Taha, Allan Hanbury

Abstract

Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,513 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 <1%
Colombia 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Finland 1 <1%
China 1 <1%
Unknown 1506 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 316 21%
Student > Master 229 15%
Student > Bachelor 151 10%
Researcher 149 10%
Student > Doctoral Student 71 5%
Other 160 11%
Unknown 437 29%
Readers by discipline Count As %
Engineering 316 21%
Computer Science 281 19%
Medicine and Dentistry 159 11%
Physics and Astronomy 59 4%
Neuroscience 31 2%
Other 149 10%
Unknown 518 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 February 2024.
All research outputs
#1,732,244
of 25,393,455 outputs
Outputs from BMC Medical Imaging
#6
of 665 outputs
Outputs of similar age
#22,075
of 277,295 outputs
Outputs of similar age from BMC Medical Imaging
#1
of 14 outputs
Altmetric has tracked 25,393,455 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 665 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,295 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.