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Multi-stage image pyramid network for coarse-to-fine segmentation of temporomandibular joint in CBCT images
Piaolin Hu1, Jupeng Li1, Ruohan Ma2
1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Objectives:
Accurate segmentation of the temporomandibular joint (TMJ) in cone beam computed tomography (CBCT) images is crucial for the diagnosis of temporomandibular joint osteoarthritis (TMJ-OA), particularly of the condyle. The aim of this study was to use a multi-stage image pyramid network for precise segmentation of the TMJ in CBCT images.
Methods:
A multi-stage image pyramid network (MIP-Net) was proposed in this study for accurate TMJ segmentation in CBCT images. This network utilizes stepwise down-sampling to form an image pyramid, and feature fusion is employed to integrate features of different scales, resulting in accurate segmentation of the TMJ in CBCT images.
Results:
The network was trained and evaluated using a clinical image dataset, and the dice similarity coefficient (DSC) was 98.02 ± 0.65 %, the average surface distance (ASD) was 0.0605 ± 0.0229 mm, and the Hausdorff distance (HD) was 0.1907 ± 0.0627 mm.
Conclusions:
The proposed MIP-Net achieved accurate segmentation of the TMJ in CBCT images, which may help to improve the clinical diagnosis and treatment of TMJ-OA.
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Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...