Methods Inf Med - Automatic correction of gaps in cerebrovascular segmentations extracted from 3D time-of-flight MRA datasets.

Tópicos

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Resumo

JECTIVES: Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations.METHODS: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation.RESULTS: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert.CONCLUSION: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

Resumo Limpo

jectiv exact cerebrovascular segment requir sever applic today clinic routin major drawback typic automat segment method occurr gap within segment gap typic locat small vessel structur exhibit low intens manual correct timeconsum suitabl clinic practic work present postprocess method automat detect close gap cerebrovascular segmentationsmethod approach d centerlin calcul avail vessel segment enabl detect correspond vessel endpoint endpoint use detect possibl connect d centerlin voxel graphbas approach consist check reason detect path expand vessel boundari use level set approach combin initi segmentationresult evalu purpos gap artifici insert nonbranch vessel bifurc manual cerebrovascular segment deriv ten timeofflight magnet reson angiographi dataset result show present method capabl detect nonbranch vessel gap bifurc gap level set segment expand detect connect mm accuraci compar initi segment evalu base real automat segment dataset show propos method detect addit connect averag per dataset wherea rate correct medic expertconclus present approach can consider improv accuraci cerebrovascular segment follow analysi outcom

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