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Automatic strategy for extraction of anthropometric measurements for the diagnostic and evaluation of deformational plagiocephaly from infant’s head models
Author(s): Bruno Oliveira; Helena R. Torres; Fernando Veloso; Estela Vilhena; Nuno F. Rodrigues; Jaime C. Fonseca; Pedro Morais; João L. Vilaça
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Paper Abstract

Deformational Plagiocephaly (DP) refers to an asymmetrical distortion of an infant’s skull resulting from external forces applied over time. The diagnosis of this condition is performed using asymmetry indexes that are estimated from specific anatomical landmarks, whose are manually defined on head models acquired using laser scans. However, this manual identification is susceptible to intra-/inter-observer variability, being also time-consuming. Therefore, automatic strategies for the identification of the landmarks and, consequently, extraction of asymmetry indexes, are claimed. A novel pipeline to automatically identify these landmarks on 3D head models and to estimate the relevant cranial asymmetry indexes is proposed. Thus, a template database is created and then aligned with the unlabelled patient through an iterative closest point (ICP) strategy. Here, an initial rigid alignment followed by an affine one are applied to remove global misalignments between each template and the patient. Next, a non-rigid alignment is used to deform the template information to the patient-specific shape. The final position of each landmark is computed as a local weight average of all candidate results. From the identified landmarks, a head’s coordinate system is automatically estimated and later used to estimate cranial asymmetry indexes. The proposed framework was evaluated in 15 synthetic infant head’s model. Overall, the results demonstrated the accuracy of the identification strategy, with a mean average distance of 2.8±0.6 mm between the identified landmarks and the ground-truth. Moreover, for the estimation of cranial asymmetry indexes, a performance comparable to the inter-observer variability was achieved.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500A (13 March 2019); doi: 10.1117/12.2512782
Show Author Affiliations
Bruno Oliveira, Polytechnic Institute of Cávado and Ave. (Portugal)
Univ. do Minho (Portugal)
Helena R. Torres, Polytechnic Institute of Cávado and Ave. (Portugal)
Univ. do Minho (Portugal)
Fernando Veloso, Polytechnic Institute of Cávado and Ave. (Portugal)
Univ. do Minho (Portugal)
Estela Vilhena, Polytechnic Institute of Cávado and Ave. (Portugal)
Univ. do Minho (Portugal)
Nuno F. Rodrigues, Polytechnic Institute of Cávado and Ave. (Portugal)
Jaime C. Fonseca, Univ. do Minho (Portugal)
Pedro Morais, Polytechnic Institute of Cávado and Ave. (Portugal)
João L. Vilaça, Polytechnic Institute of Cávado and Ave. (Portugal)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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