Teeth Structure Segmentation Based on Multi-Source Semi-Supervised Learning

doi:10.11871/jfdc.issn.2096-742X.2025.02.017

Abstract

Abstract:

[Objective] Current research in dental structure segmentation faces two primary constraints: the prohibitively high costs of annotated dental data and the inherent complexity of multi-source datasets. While semi-supervised learning approaches have been widely adopted for constructing deep learning networks using multi-source data, effectively extracting discriminative features from heterogeneous unlabeled data remains a significant challenge. [Methods] Building upon the Mean-Teacher framework, we propose a novel Multi-Source Semi-Supervised Learning algorithm that incorporates, a domain-adaptive consistency regularization mechanism, and a hybrid loss function combining structural similarity and uncertainty-aware constraints to optimize model training. [Results] Comprehensive evaluation on a clinically acquired dataset demonstrated superior segmentation performance, achieving mean scores of: Dice 81.13%, Jaccard 82.96%, recall 82.78%, and precision 82.91%. Statistical analysis confirmed significant improvements over other methods. [Conclusions] Compared to mainstream methods that directly incorporate unlabeled data into training, the Multi-Student model, based on multi-source semi-supervised learning, can better enhance the accuracy of semi-supervised tooth segmentation models.

Key words: artificial intelligence, medical imaging, transfer learning, deep learning, tooth segmentation

JIA Ziang. Teeth Structure Segmentation Based on Multi-Source Semi-Supervised Learning[J]. Frontiers of Data and Computing, 2025, 7(2): 175-185, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.02.017.

Figures/Tables 11

Fig.1

Table 1

Fig.2

Table 2

CBAM-Unet Basic Model Setting"

Layer	Output Shape	Operation Type	Configuration
Input	(C, H, W)	Input Image	-
enc1	(16, H, W)	2×Conv+BN+ReLU	$C i n = 3, C o u t = 16$
enc2	(32, H/2, W/2)	2×Conv+BN+ReLU	$C i n = 16, C o u t = 32$
enc3	(64, H/4, W/4)	2×Conv+BN+ReLU	$C i n = 32, C o u t = 64$
enc4	(128, H/8, W/8)	2×Conv+BN+ReLU	$C i n = 64, C o u t = 128$
center	(256, H/16,W/16)	2×Conv+BN+ReLU	$C i n = 128, C o u t = 256$
dec4	(128, H/8, W/8)	2×Conv+BN+ReLU	$C i n = 256, C o u t = 128$
dec3	(64, H/4, W/4)	2×Conv+BN+ReLU	$C i n = 128, C o u t = 64$
dec2	(32, H/2, W/2)	2×Conv+BN+ReLU	$C i n = 64, C o u t = 32$
dec1	(16, H, W)	2×Conv+BN+ReLU	$C i n = 32, C o u t = 16$
Output	(8, H, W)	1×Conv	$C i n = 16, C o u t = 8$

Table 2

Fig.3

Table 3

Fig.4

Fig.5

Table 4

Fig.6

Table 5

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	已标注数据	未标注数据
3D CBCT牙齿数据集	120	30
STS3D2024数据集	30	300

	已标注数据	未标注数据
X1	150	0
X2	150	330
X3	0	330

	Dice(%)	Jaccard(%)	Recall(%)	Precision(%)
CCT^[28]	80.16	82.54	80.28	82.43
TCSM^[29]	80.10	81.29	81.04	80.96
ECMT^[30]	80.97	82.34	82.10	81.63
Multi-Student (Ours)	81.13	82.96	82.78	82.91

	Dice(%)	Jaccard(%)	Recall(%)	Precision(%)
Mean-Teacher	75.34	74.61	73.17	73.90
Multi-Student+MSE	79.84	79.19	78.33	80.87
Multi-Student (Ours)	81.13	82.96	82.78	82.91