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Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning

Overview of attention for article published in Radiation Oncology, January 2016
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Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning
Published in
Radiation Oncology, January 2016
DOI 10.1186/s13014-015-0579-1
Pubmed ID

Joris Van de Velde, Johan Wouters, Tom Vercauteren, Werner De Gersem, Eric Achten, Wilfried De Neve, Tom Van Hoof


The present study aimed to define the optimal number of atlases for automatic multi-atlas-based brachial plexus (BP) segmentation and to compare Simultaneous Truth and Performance Level Estimation (STAPLE) label fusion with Patch label fusion using the ADMIRE® software. The accuracy of the autosegmentations was measured by comparing all of the generated autosegmentations with the anatomically validated gold standard segmentations that were developed using cadavers. Twelve cadaver computed tomography (CT) atlases were used for automatic multi-atlas-based segmentation. To determine the optimal number of atlases, one atlas was selected as a patient and the 11 remaining atlases were registered onto this patient using a deformable image registration algorithm. Next, label fusion was performed by using every possible combination of 2 to 11 atlases, once using STAPLE and once using Patch. This procedure was repeated for every atlas as a patient. The similarity of the generated automatic BP segmentations and the gold standard segmentation was measured by calculating the average Dice similarity (DSC), Jaccard (JI) and True positive rate (TPR) for each number of atlases. These similarity indices were compared for the different number of atlases using an equivalence trial and for the two label fusion groups using an independent sample-t test. DSC's and JI's were highest when using nine atlases with both STAPLE (average DSC = 0,532; JI = 0,369) and Patch (average DSC = 0,530; JI = 0,370). When comparing both label fusion algorithms using 9 atlases for both, DSC and JI values were not significantly different. However, significantly higher TPR values were achieved in favour of STAPLE (p < 0,001). When fewer than four atlases were used, STAPLE produced significantly lower DSC, JI and TPR values than did Patch (p = 0,0048). Using 9 atlases with STAPLE label fusion resulted in the most accurate BP autosegmentations (average DSC = 0,532; JI = 0,369 and TPR = 0,760). Only when using fewer than four atlases did the Patch label fusion results in a significantly more accurate autosegmentation than STAPLE.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Researcher 6 22%
Other 4 15%
Student > Master 3 11%
Student > Bachelor 3 11%
Other 4 15%
Unknown 1 4%
Readers by discipline Count As %
Medicine and Dentistry 16 59%
Physics and Astronomy 3 11%
Mathematics 1 4%
Agricultural and Biological Sciences 1 4%
Sports and Recreations 1 4%
Other 3 11%
Unknown 2 7%