雨果巴拉:行业北极星Vision Pro过度设计不适合市场

Automatic Dental Plaque Segmentation Based on Local-to-Global Features Fused Self-Attention Network

Note: We don't have the ability to review paper

PubDate: January 2022

Teams: Beihang University;Beijing Information Science and Technology University;Peking University School and Hospital of Stomatology;Stony Brook University

Writers: Shuai Li; Yuting Guo; Zhennan Pang; Wenfeng Song; Aimin Hao; Bin Xia; Hong Qin

PDF: Automatic Dental Plaque Segmentation Based on Local-to-Global Features Fused Self-Attention Network

Abstract

The accurate detection of dental plaque at an early stage will definitely prevent periodontal diseases and dental caries. However, it remains difficult for the current dental examination to accurately recognize dental plaque without using medical dyeing reagent due to the low contrast between dental plaque and healthy teeth. To combat this problem, this paper proposes a novel network enhanced by a self-attention module for intelligent dental plaque segmentation. The key motivation is to directly utilize oral endoscope images (bypassing the need for dyeing reagent) and get accurate pixel-level dental plaque segmentation results. The algorithm needs to conduct self-attention at the super-pixel level and fuse the super-pixels’ local-to-global features. Our newly-designed network architecture will afford the simultaneous fusion of multiple-scale complementary information guided by the powerful deep learning paradigm. The critical fused information includes the statistical distribution of the plaques color, the heat kernel signature (HKS) based local-to-global structure relationship, and the circle-LBP based local texture pattern in the nearby regions centering around the plaque area. To further refine the fuzed multiple-scale features, we devise an attention module based on CNN, which could focalize the regions of interest in plaque more easily, especially for many challenging cases. Extensive experiments and comprehensive evaluations confirm that, for a small-scale training dataset, our method could outperform the state-of-the-art methods. Meanwhile, the user studies verify the claim that our method is more accurate than conventional dental practice conducted by experienced dentists.

您可能还喜欢...

Paper