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Outline
To address the issue of existing deep learning-based segmentation methods for dental models, which overly rely on coordinate features and overlook the differences and complementary nature of geometric and coordinate features, a segmentation network based on attention mechanism and neighbor enhancement is proposed. First, an attention-enhanced module is employed to enhance the neighborhood of input features through different encoding schemes. It designs a differential attention mechanism and a self-attention mechanism to extract comprehensive contextual features of the mesh by considering the properties of geometric and coordinate features separately. Next, a complementary pooling function is constructed to fuse maximum pooling, attention pooling, and average pooling, which promotes the complementary interaction among different features and enhances the robustness of the segmentation network. Finally, an adaptive attention module is embedded to balance the numerical differences among multi-scale features for effective mesh segmentation. Compared with other methods in 200 real patients dental model dataset, the segmentation accuracy of dental model is 96.18%. The experimental results show that the proposed method is superior to the compared deep learning networks and has better performance in the segmentation of dental models.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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