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

• Type: Article in International Scientific Journal
• Date: 2018-07

Authors

Pedro Morais (Author) Other; João L. Vilaça (Author) Other; Sandro Queirós (Author) Other; Alberto Marchi (Author) Other; Felix Bourier (Author) Other; Isabel Deisenhofer (Author) Other; Jan D'hooge (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Computer Methods and Programs in Biomedicine

Vol. 161

Pages: 73-84

ISSN: 0169-2607

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 1 Citation

ISI Web of Science

Scopus

INSPEC

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: P-00R-A8E

DOI: 10.1016/j.cmpb.2018.04.014

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12