In recent years, there has been an increasing body of research highlighting the close relationship between the diagnosis of osteoporosis and the lower jaw. The study of the jawbone in dentistry has gained increasing popularity and has become a focus for artificial intelligence applications. This study, employing a collective intelligence modality, achieved pixel-level fusion by integrating three recently prominent segmentation methods for mandible and maxillomandibular segmentation. Two publicly available third-party datasets were utilized for mandible and maxillomandibular segmentation. The collective intelligence modality underwent analysis on both hard label (0 or 1) and soft label (0-1) at the pixel level through decision fusion. Ultimately, it was observed that the decision fusion process with soft labels surpassed the performance of both single modality and hard label decision fusion. In ablation studies, the threshold parameter for transitioning from soft label to hard label in binary segmentation was determined by assessing the model’s dice score performance using the grid search approach. As a result of the experimental studies, the dice score of the Soft Label Decision Fusion by mean model (SLDF mean), SLDFNet, which achieved the highest performance on both datasets, was 0. 9821 for the mandible and 0. 9782 for the maxillomandibular region, respectively. The binary segmentation threshold parameter of SLDFNet in both datasets was determined to be 0. 4 through experiments. According to the results, it seems that the proposed method provides state-of-the-art outcomes. The robustness of the proposed method has been demonstrated on two datasets, each with relatively few and large amounts of data compared to the other.
Kumbasar et al. (Mon,) studied this question.