Semantic segmentation for tooth cracks using improved DeepLabv3+ model.

ABSTRACT:

Objective

Accurate and prompt detection of cracked teeth plays a critical role for human oral health. The aim of this paper is to evaluate the performance of a tooth crack segmentation model (namely, FDB-DeepLabv3+) on optical microscopic images.

Method

The FDB-DeepLabv3+ model proposed here improves feature learning by replacing the backbone with ResNet50. Feature pyramid network (FPN) is introduced to fuse muti-level features. Densely linked atrous spatial pyramid pooling (Dense ASPP) is applied to achieve denser pixel sampling and wider receptive field. Bottleneck attention module (BAM) is embedded to enhance local feature extraction.

Results

Through testing on a self-made hidden cracked tooth dataset, the proposed method outperforms four classical networks (FCN, U

SUBMITTER: Xie Z 

PROVIDER: S-EPMC10877285 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature