Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions

• Autor(a) principal: Morais, Pedro
• Data de Publicação: 2018
• Outros Autores: Vilaça, João, Queirós, Sandro, Marchi, Alberto, Bourier, Felix, Deisenhofer, Isabel, D'hooge, Jan, Tavares, João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/11110/1415
• topic: Image segmentation; Cardiac Intervention Planning; Inter-atrial wall interventions; Competitive segmentation strategy; Atlas-based initialization

Background and objective: Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO). Methods: The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strat- egy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location. Results: The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The auto- matic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO esti- mation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free. Conclusions: Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice.