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15 February 2021 FlyBy CNN: a 3D surface segmentation framework
Louis Boubolo, Maxime Dumont, Serge Brosset, Jonas Bianchi, Antonio Ruellas, Marcela Gurgel, Camila Massaro, Aron Aliaga Del Castillo, Marcos Ioshida, Marilia Yatabe, Erika Benavides, Hector Rios, Fabiana Soki, Gisele Neiva, Beatriz Paniagua, Lucia Cevidanes, Martin Styner, Juan Carlos Prieto
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Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962B (2021) https://doi.org/10.1117/12.2582205
Event: SPIE Medical Imaging, 2021, Online Only
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Abstract
In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyBy-CNN consists of sampling the surface of the 3D object from different view points and extracting surface featuressuch as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networkssuch as RUNETs. We test our framework in a dental application for segmentation of intra-oral surfaces. TheRUNET is trained for the segmentation task using image pairs of surface features and image labels as groundtruth. The resulting labels from each segmented image are put back into the surface thanks to our samplingapproach that generates 1-1 correspondence of image pixels and triangles in the surface model. The segmentationtask achieved an accuracy of 0.9
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Louis Boubolo, Maxime Dumont, Serge Brosset, Jonas Bianchi, Antonio Ruellas, Marcela Gurgel, Camila Massaro, Aron Aliaga Del Castillo, Marcos Ioshida, Marilia Yatabe, Erika Benavides, Hector Rios, Fabiana Soki, Gisele Neiva, Beatriz Paniagua, Lucia Cevidanes, Martin Styner, and Juan Carlos Prieto "FlyBy CNN: a 3D surface segmentation framework", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962B (15 February 2021); https://doi.org/10.1117/12.2582205
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KEYWORDS
Image segmentation
Convolutional neural networks
Image analysis
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