Autor(es): Morais, Pedro ; Vilaça, João ; Queirós, Sandro ; Marchi, Alberto ; Bourier, Felix ; Deisenhofer, Isabel ; D'hooge, Jan ; Tavares, João
Data: 2018
Identificador Persistente: http://hdl.handle.net/11110/1415
Origem: CiencIPCA
Assunto(s): 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.
The authors acknowledge Fundacão para a Ciência e a Tec- nologia (FCT), in Portugal, and the European Social Found, Eu- ropean Union, for funding support through the “Programa Op- eracional Capital Humano” (POCH) in the scope of the PhD grants SFRH/BD/95438/2013 (P. Morais) and SFRH/BD/93443/2013 (S. Queirós). This work was funded by projects NORTE-01-0145-FEDER- 000013, NORTE-01-0145-FEDER-000022 and NORTE-01-0145- FEDER-024300, supported by Northern Portugal Regional Oper- ational Programme (Norte2020), under the Portugal 2020 Part- nership Agreement, through the European Regional Development Fund (FEDER), and also been funded by FEDER funds, through Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the FCT, under the scope of the project POCI-01-0145-FEDER-007038.
