3D Temporomandibular Joint CBCT Image Segmentation via Multi-directional Resampling Ensemble Learning Network

Accurate segmentation of temporomandibular joint (TMJ) from dental cone beam CT (CBCT) images is basis of for early diagnosis of TMJ-related diseases such as temporomandibular disorders (TMD). Fully convolutional networks (FCN) have achieved the state-of-the-art performance in medical image segmentation field. Both enough contextual information as well as rich spatial semantic information is required to obtain accurate segmentation, however, due to the limited GPU memories, high-resolution 3D volume cannot be directly input to these models. In this paper, we propose Multi-directional Resampling Ensemble Learning Network for 3D TMJ-CBCT image segmentation. This model extracts four semantic features from multi-directional resampled volumes, and then integrates features via ensemble learning network to achieve accurate segmentation. We implement extensive evaluations of the proposed method on a clinical images dataset, including images acquired from 89 patients. Our method achieves the Mean DSC value of 0.9814 ± 0.0054, the Mean Hausdorff Distance of 1.5711 ± 1.0252 mm, and the Mean Average Surface Distance of 0.0555 ± 0.0198 mm.