Mask-MCNet

Autor: Farhad Ghazvinian Zanjani, Arash Pourtaherian, Svitlana Zinger, David Anssari Moin, Frank Claessen, Teo Cherici, Sarah Parinussa, Peter H.N. de With
Přispěvatelé: Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Signal Processing Systems, Eindhoven MedTech Innovation Center, EAISI Health
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Neurocomputing, 453, 286-298. Elsevier
ISSN: 0925-2312
Popis: Computational dentistry uses computerized methods and mathematical models for dental image analysis. One of the fundamental problems in computational dentistry is accurate tooth instance segmentation in high-resolution mesh data of intra-oral scans (IOS). This paper presents a new computational model based on deep neural networks, called Mask-MCNet, for end-to-end learning of tooth instance segmentation in 3D point cloud data of IOS. The proposed Mask-MCNet localizes each tooth instance by predicting its 3D bounding box and simultaneously segments the points that belong to each individual tooth instance. The proposed model processes the input raw 3D point cloud in its original spatial resolution without employing a voxelization or down-sampling technique. Such a characteristic preserves the finely detailed context in data like fine curvatures in the border between adjacent teeth and leads to a highly accurate segmentation as required for clinical practice (e.g. orthodontic planning). The experiments show that the Mask-MCNet outperforms state-of-the-art models by achieving 98% Intersection over Union (IoU) score on tooth instance segmentation which is very close to human expert performance.
Databáze: OpenAIRE
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