[Automatic micro-CT pulp cavity image segmentation based on few-shot transfer learning]

Objective: To develop a micro-CT pulp cavity image segmentation model based on few-shot transfer learning, enabling efficient and accurate segmentation of the pulp cavity with limited training samples, thereby supporting three-dimensional(3D) anatomical research of the pulp cavity and digital root canal treatment. Methods: Extracted teeth (n=110) due to pathological reasons were collected from the Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Wuhan University, between January 2025 and September 2025. These teeth were scanned using micro-CT. The acquired images were randomly divided (simple random sampling) into a training set (10 teeth), a test set (90 teeth), and an independent test set (10 teeth) at a ratio of 1∶9∶1. 3D pulp cavity images were meticulously annotated by two oral clinicians (attending physicians) to establish the ground truth for segmentation. By introducing a cross-domain adaptation strategy, the natural image segmentation foundation model, segment anything model (SAM), was transferred to the task of pulp cavity image segmentation under the guidance of a limited number of training samples, thereby constructing an automatic micro-CT pulp cavity image segmentation model (PulpSAM). Multi-dimensional quantitative and qualitative analyses were performed to compare the segmentation performance on the pulp cavity (entire pulp cavity, apical 3 mm, and lateral accessory canals) achieved by deep learning models obtained with different numbers of training samples (0, 1, 3, and 10 teeth), i.e., SAM, PulpSAM-1, PulpSAM-3, and PulpSAM-10. These results were also compared with those of the U-Net and nnU-Net models. Additionally, the time required for three methods (manual annotation, fully automated model segmentation, and model segmentation followed by manual refinement) was compared. Results: Under training sample sizes of 1, 3, and 10 teeth, all PulpSAM models achieved high segmentation performance, with median Dice coefficient, Intersection over Union (IoU), precision, recall, and accuracy all≥92.3%, median 95% Hausdorff distance (95%HD)≤0.04 mm, and median average symmetric surface distance (ASSD) of 0.02 mm. These metrics were significantly superior to those of the SAM model (all P<0.008 3). The pulp cavity volume and relative volume difference (RVD) segmented by each PulpSAM model were significantly smaller than those of the SAM model (all P<0.008 3). Except for ASSD and precision, the other seven segmentation accuracy metrics of PulpSAM-3 were significantly better than those of PulpSAM-1 (all P<0.008 3). Compared with PulpSAM-10, PulpSAM-3 showed significantly higher recall, ASSD, and pulp cavity volume (all P<0.008 3), and significantly lower precision and absolute RVD (all P<0.008 3). No statistically significant differences were observed between PulpSAM-3 and PulpSAM-10 in Dice coefficient, IoU, accuracy, or 95%HD (all P>0.008 3). All segmentation accuracy metrics of each PulpSAM model were significantly superior to those of the corresponding U-Net and nnU-Net models (all P<0.017). The segmentation times required for manual annotation, PulpSAM-3 automatic segmentation, and PulpSAM-3 segmentation followed by manual refinement were 3 354.6 (852.0), 190.0 (27.9), 646.4 (171.5) s, respectively, with statistically significant differences among the three methods (χ²=25.80, P<0.001). Conclusions: This study developed the PulpSAM model based on a few-shot transfer learning strategy, achieving efficient and accurate automatic segmentation of the pulp cavity in micro-CT images.