MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy
Authors: Kyeonghun Kim, Jaehyung Park, Youngung Han, Anna Jung, Seongbin Park, Sumin Lee, Jiwon Yang, Jiyoon Han, +12 more
Organizations: OUTTA, Seoul, South Korea · Seoul National University, Seoul, South Korea · Sangmyung University, Seoul, South Korea · Gwangju Institute of Science and Technology, Gwangju, South Korea · Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea · Samsung Changwon Hospital, Sungkyunkwan University, Changwon, South Korea · NVIDIA AI Technology Center, Taipei, Taiwan
Dental diagnosis from Orthopantomograms (OPGs) requires coordination of tooth detection, caries segmentation (CarSeg), anomaly detection (AD), and dental developmental staging (DDS). We propose Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy (MATHENA), a unified framework leveraging Mamba's linear-complexity State Space Models (SSM) to address all four tasks. MATHENA integrates MATHE, a multi-resolution SSM-driven detector with four-directional Vision State Space (VSS) blocks for O(N) global context modeling, generating per-tooth crops. These crops are processed by HENA, a lightweight Mamba-UNet with a triple-head architecture and Global Context State Token (GCST). In the triple-head architecture, CarSeg is first trained as an upstream task to establish shared representations, which are then frozen and reused for downstream AD fine-tuning and DDS classification via linear probing, enabling stable, efficient learning. We also curate PARTHENON, a benchmark comprising 15,062 annotated instances from ten datasets. MATHENA achieves 93.78% mAP@50 in tooth detection, 90.11% Dice for CarSeg, 88.35% for AD, and 72.40% ACC for DDS.
Figures & tables
Figure 1: Quantitative results on PARTHENON: (a) MATHE variants outperform baseline models in tooth detection (mAP50); (b, c) MATHENA variants show superior Dice scores in CarSeg and AD. ( † : baseline enhanced with P2, BiFPN, and WIoU).
Figure 2: MATHENA architecture: Left shows MATHE backbone, BiFPN, and detection head; Right depicts HENA encoder-decoder with GCST skip fusion. SRA (Spatial Re-Alignment) remaps the output mask to the original OPG for visualization.
Method
Backbone
mAP 50 (%)
mAP 75 (%)
mAP 50:95 (%)
RetinaNet
ResNet-50
71.01
61.81
51.83
YOLOv8n ⋆
CSPDarkNet(C2f)
71.30
66.30
61.90
FCOS
ResNet-50
71.86
62.20
53.20
SSD300
VGG-16
72.77
65.60
58.80
YOLOv8s ⋆
CSPDarkNet(C2f)
73.66
70.05
65.02
Faster R-CNN
MobileNetV3
80.08
66.56
49.30
Table 2: Tooth detection on PARTHENON: ⋆ baseline YOLOv8; † enhanced with P2, BiFPN, WIoU.
CarSeg
AD
DDS
Method
Dice (%)
IoU (%)
Dice (%)
IoU (%)
ACC (%)
F1 (%)
FPN
76.41
63.93
74.74
61.29
–
–
MAnet
77.51
64.48
75.29
62.03
–
–
PSPNet
79.21
66.12
76.55
63.37
–
–
UNet
80.81
67.24
78.16
64.65
–
–
Linknet
82.21
68.75
79.87
66.14
–
–
Table 3: Multi-task performance on PARTHENON: CarSeg and AD averaged across subsets; DDS on DDSNet.
Figure 3: Visual comparison: Left shows Tooth Detection; Right depicts CarSeg.
Configuration
Detection (%)
CarSeg (%)
AD (%)
DDS (%)
Full MATHENA
93.78
90.11
88.35
72.40
w/o GCST in MATHE
89.42
90.05
88.29
72.36
w/o Mamba in HENA BN
93.74
87.75
85.99
69.95
w/o GCST skip fusion
93.75
86.96
85.20
72.32
w/ standard FPN(no BiFPN)
90.64
90.03
88.27
72.25
w/ Transformer(vs. Mamba)
92.13
88.66
86.90
70.94
Table 4: Ablation study on PARTHENON: impact of each component across all tasks.
Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland · Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
Faculty of Dentistry, The University of Hong Kong, Hong Kong, China · Department of Computing, Imperial College London, London, United Kingdom · University of Science and Technology of China, Hefei, China +2