Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

Velde, Jan Van de; Wouters, Jan; Vercauteren, Tom; Gersem, Werner De; Duprez, Frédéric; Neve, Wilfried De; Hoof, Tom Van

doi:10.1016/j.ijrobp.2015.02.045

Cited by 4 publications

(8 citation statements)

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“…In ADMIRE®, two label fusion algorithms can be applied: the Simultaneous Truth and Performance Level Estimation (STAPLE) [ 10 ] and Patch [ 11 ] label fusion. STAPLE was proven to be more effective than Patch label fusion for multi-atlas-based BP autosegmentation [ 3 ]. This label fusion algorithm was originally designed for the validation of image segmentations.…”

Section: Methodsmentioning

confidence: 99%

“…In the first step, in order to measure the effect of a morphometric atlas selection strategy implementation in multi-atlas-based BP segmentation, the protraction distances were measured for all atlases [ 3 ]. The protraction distance is defined as the horizontal distance measured in the sagittal plane between a vertical line through the anterior tubercle of C5 and a vertical line through the infraglenoid tubercle (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…With JI, on the other hand, the amount of correctly delineated volume associated with an increment or decrement of 10 % of the JI value will vary depending on the starting value of the JI, because this index has a non-linear course. The inclusion index was not adequate for the equivalence trial because the highest INI value does not necessary imply the most accurate segmentation [ 3 ].…”

Section: Methodsmentioning

confidence: 99%

“…Optimal deformable image registration and label fusion algorithms need to be chosen, the optimal number of atlases need to be determined, and the most patient-similar atlases need to be selected for registration. In earlier publications [ 3 ], the best label fusion algorithm and optimal number of atlases for automatic BP segmentation without atlas selection were already determined in ADMIRE® software. However, the effect of implementation of an atlas selection strategy on multi-atlas-based BP autosegmentation accuracy has not yet been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…An organ-specific atlas preselection strategy based on morphometric parameters was developed for the BP by Van de Velde et al [ 3 ]. The authors measured several morphometric parameters related to the BP on atlas- and patient-Computed Tomography (CT) and investigated which parameters significantly improved the autosegmentation result.…”

Section: Introductionmentioning

confidence: 99%

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The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

et al. 2015

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PurposeThe present study aimed to measure the effect of a morphometric atlas selection strategy on the accuracy of multi-atlas-based BP autosegmentation using the commercially available software package ADMIRE® and to determine the optimal number of selected atlases to use. Autosegmentation accuracy was measured by comparing all generated automatic BP segmentations with anatomically validated gold standard segmentations that were developed using cadavers.Materials and methodsTwelve cadaver computed tomography (CT) atlases were included in the study. One atlas was selected as a patient in ADMIRE®, and multi-atlas-based BP autosegmentation was first performed with a group of morphometrically preselected atlases. In this group, the atlases were selected on the basis of similarity in the shoulder protraction position with the patient. The number of selected atlases used started at two and increased up to eight. Subsequently, a group of randomly chosen, non-selected atlases were taken. In this second group, every possible combination of 2 to 8 random atlases was used for multi-atlas-based BP autosegmentation. For both groups, the average Dice similarity coefficient (DSC), Jaccard index (JI) and Inclusion index (INI) were calculated, measuring the similarity of the generated automatic BP segmentations and the gold standard segmentation. Similarity indices of both groups were compared using an independent sample t-test, and the optimal number of selected atlases was investigated using an equivalence trial.ResultsFor each number of atlases, average similarity indices of the morphometrically selected atlas group were significantly higher than the random group (p < 0,05). In this study, the highest similarity indices were achieved using multi-atlas autosegmentation with 6 selected atlases (average DSC = 0,598; average JI = 0,434; average INI = 0,733).ConclusionsMorphometric atlas selection on the basis of the protraction position of the patient significantly improves multi-atlas-based BP autosegmentation accuracy. In this study, the optimal number of selected atlases used was six, but for definitive conclusions about the optimal number of atlases and to improve the autosegmentation accuracy for clinical use, more atlases need to be included.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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