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Deep learning-based detection of anthropometric landmarks in 3D infants head models
Author(s): Helena R. Torres; Bruno Oliveira; Fernando Veloso; Mario Ruediger; Wolfram Burkhardt; António Moreira; Nuno Dias; Pedro Morais; Jaime C. Fonseca; João L. Vilaça
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Paper Abstract

Deformational plagiocephaly (DP) is a cranial deformity characterized by an asymmetrical distortion of an infant’s skull. The diagnosis and evaluation of DP are performed using cranial asymmetry indexes obtained from cranial measurements, which can be estimated using anthropometric landmarks of the infant’s head. However, manual labeling of these landmarks is a time-consuming and tedious task, being also prone to observer variability. In this paper, a novel framework to automatically detect anthropometric landmarks of 3D infant’s head models is described. The proposed method is divided into two stages: (i) unfolding of the 3D head model surface; and (ii) landmarks’ detection through a deep learning strategy. In the first stage, an unfolding strategy is used to transform the 3D mesh of the head model to a flattened 2D version of it. From the flattened mesh, three 2D informational maps are generated using specific head characteristics. In the second stage, a deep learning strategy is used to detect the anthropometric landmarks in a 3-channel image constructed using the combination of informational maps. The proposed framework was validated in fifteen 3D synthetic models of infant’s head, being achieved, in average for all landmarks, a mean distance error of 3.5 mm between the automatic detection and a manually constructed ground-truth. Moreover, the estimated cranial measurements were comparable to the ones obtained manually, without statistically significant differences between them for most of the indexes. The obtained results demonstrated the good performance of the proposed method, showing the potential of this framework in clinical practice.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095034 (13 March 2019); doi: 10.1117/12.2512196
Show Author Affiliations
Helena R. Torres, Instituto Politécnico do Cávado e do Ave (Portugal)
Univ. do Minho (Portugal)
Bruno Oliveira, Instituto Politécnico do Cávado e do Ave (Portugal)
Univ. do Minho (Portugal)
Fernando Veloso, Instituto Politécnico do Cávado e do Ave (Portugal)
Univ. do Minho (Portugal)
Mario Ruediger, Children's Hospital (Germany)
Wolfram Burkhardt, Children's Hospital (Germany)
António Moreira, Instituto Politécnico do Cávado e do Ave (Portugal)
Nuno Dias, Instituto Politécnico do Cávado e do Ave (Portugal)
Pedro Morais, Instituto Politécnico do Cávado e do Ave (Portugal)
Jaime C. Fonseca, Univ. do Minho (Portugal)
João L. Vilaça, Instituto Politécnico do Cávado e do 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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