Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions. (July 2018) - Morais, Pedro; Vilaça, João L.; Queirós, Sandro; Marchi, Alberto; Bourier, Felix; Deisenhofer, Isabel; D'hooge, Jan; Tavares, João Manuel R.S.
Highlights: A novel fully automatic (FA) strategy to segment the relevant anatomical models, atrial region and fossa ovalis (FO). The novel method starts by segmenting the atrial region using an atlas-based strategy followed by a competitive deformable model approach. The FO is estimated based on the atrial region models, by combining patient-specific information and the expected FO spatial location. High accuracy and feasibility in a clinical database, and performance similar to the inter-observer variability and to other state-of-the-art methods were found. The proposed method showed its potential for the planning of inter-atrial septal interventions. Abstract: 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 strategy. 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 automatic 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 estimation 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. Graphical abstract: Overview of the proposed fully automatic method for atrial region segmentation (red: left atrium, green: right atrium, blue: aortic tract) and fossa ovalis identification (yellow).