FlyBy CNN: a 3D surface segmentation framework | Zendy

Louis Boubolo | Zendy; Maxime Dumont | Zendy; Serge Brosset | Zendy; Jonas Bianchi | Zendy; Antônio Carlos de Oliveira Ruellas | Zendy; Marcela Gurgel | Zendy; Camila Massaro | Zendy; Aron Aliaga | Zendy; Marcos Ioshida | Zendy; Marília Yatabe | Zendy; Erika Benavides | Zendy; Héctor F. Ríos | Zendy; Fabia. Soki | Zendy; Gisele F. Neiva | Zendy; Beatriz Paniagua | Zendy; Lucía Cevidanes | Zendy; Martin Styner | Zendy; Juan Carlos Prieto | Zendy

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FlyBy CNN: a 3D surface segmentation framework

Author(s) -

Louis Boubolo,

Maxime Dumont,

Serge Brosset,

Jonas Bianchi,

Antônio Carlos de Oliveira Ruellas,

Marcela Gurgel,

Camila Massaro,

Aron Aliaga,

Marcos Ioshida,

Marília Yatabe,

Erika Benavides,

Héctor F. Ríos,

Fabia. Soki,

Gisele F. Neiva,

Beatriz Paniagua,

Lucía Cevidanes,

Martin Styner,

Juan Carlos Prieto

Publication year - 2021

Publication title -

medical imaging 2022: image processing

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

pISSN - 0277-786X

DOI - 10.1117/12.2582205

Subject(s) - artificial intelligence , computer science , segmentation , computer vision , convolutional neural network , surface (topology) , image segmentation , pixel , object (grammar) , image (mathematics) , deep learning , pattern recognition (psychology) , scale space segmentation , mathematics , geometry

In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyByCNN consists of sampling the surface of the 3D object from different view points and extracting surface features such as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networks such as RUNETs. We test our framework in a dental application for segmentation of intra-oral surfaces. The RUNET is trained for the segmentation task using image pairs of surface features and image labels as ground truth. The resulting labels from each segmented image are put back into the surface thanks to our sampling approach that generates 1-1 correspondence of image pixels and triangles in the surface model. The segmentation task achieved an accuracy of 0.9.

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