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

作者：Morais Pedro; Vilaca Joao L; Queiros Sandro; Marchi Alberto; Bourier Felix; Deisenhofer Isabel; D'hooge Jan; Tavares Joao Manuel R S^*

来源：Computer Methods and Programs in Biomedicine, 2018, 161: 73-84.

DOI：10.1016/j.cmpb.2018.04.014

• 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 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.

• 出版日期2018-7
• 单位KU Leuven