Int J Comput Assist Radiol Surg - Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations.

Tópicos

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Resumo

RPOSE: Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D.METHODS: The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets.RESULTS: A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients.CONCLUSION: The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.

Resumo Limpo

rpose exact knowledg nidus arterioven malform avm connect vessel often requir imagebas research project optim therapi plan aim work present evalu computeraid nidus segment techniqu subsequ angiograph character connect vessel can visual dmethod propos method develop evalu base dataset patient avm dataset consist highresolut d d magnet reson angiographi mra imag sequenc automat cerebrovascular segment d mra dataset voxelwis support vector machin classif base four extract featur perform generat new paramet map nidus repres posit valu paramet map can extract use volum grow final nidus segment dilat use automat identif feed arteri drain vein integr hemodynam inform d mra datasetsresult quantit comparison computeraid avm nidus segment result manual goldstandard segment two observ reveal mean dice coeffici compar interobserv agreement mean dice coeffici determin angiograph character visual rate feasibl patientsconclus present computeraid method enabl reproduc fast extract avm nidus well automat angiograph character connect vessel can use support imagebas research project therapi plan avm

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