Convolutional neural networks are increasingly used in the medical field for the automatic segmentation of several anatomical regions on diagnostic and non-diagnostic images. Such automatic algorithms allow to speed up time-consuming processes and to avoid the presence of expert personnel, reducing time and costs. The present work proposes the use of a convolutional neural network, the U-net architecture, for the segmentation of ear elements. The auricular elements segmentation process is a crucial step of a wider procedure, already automated by the authors, that has as final goal the realization of surgical guides designed to assist surgeons in the reconstruction of the external ear. The segmentation, performed on depth map images of 3D ear models, aims to define of the contour of the helix, antihelix, tragus-antitragus and concha. A dataset of 131 ear depth map was created;70% of the data are used as the training set, 15% composes the validation set, and the remaining 15% is used as testing set. The network showed excellent performance, achieving 97% accuracy on the validation test.

U-net for auricular elements segmentation: a proof-of-concept study / Michaela Servi; Elisa Mussi; Roberto Magherini; Monica Carfagni; Rocco Furferi; Yary Volpe. - ELETTRONICO. - (2021), pp. 2750-2755. ((Intervento presentato al convegno 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 [10.1109/EMBC46164.2021.9629834].

U-net for auricular elements segmentation: a proof-of-concept study

Michaela Servi;Elisa Mussi;Roberto Magherini;Monica Carfagni;Rocco Furferi;Yary Volpe
2021

Abstract

Convolutional neural networks are increasingly used in the medical field for the automatic segmentation of several anatomical regions on diagnostic and non-diagnostic images. Such automatic algorithms allow to speed up time-consuming processes and to avoid the presence of expert personnel, reducing time and costs. The present work proposes the use of a convolutional neural network, the U-net architecture, for the segmentation of ear elements. The auricular elements segmentation process is a crucial step of a wider procedure, already automated by the authors, that has as final goal the realization of surgical guides designed to assist surgeons in the reconstruction of the external ear. The segmentation, performed on depth map images of 3D ear models, aims to define of the contour of the helix, antihelix, tragus-antitragus and concha. A dataset of 131 ear depth map was created;70% of the data are used as the training set, 15% composes the validation set, and the remaining 15% is used as testing set. The network showed excellent performance, achieving 97% accuracy on the validation test.
43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Goal 3: Good health and well-being
Michaela Servi; Elisa Mussi; Roberto Magherini; Monica Carfagni; Rocco Furferi; Yary Volpe
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1251451
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