ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images (2307.01979v1)
Abstract: In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments. Tooth segmentation from cone-beam computed tomography (CBCT) images is a crucial step in constructing the models. However, CBCT image quality problems such as metal artifacts and blurring caused by shooting equipment and patients' dental conditions make the segmentation difficult. In this paper, we propose ToothSegNet, a new framework which acquaints the segmentation model with generated degraded images during training. ToothSegNet merges the information of high and low quality images from the designed degradation simulation module using channel-wise cross fusion to reduce the semantic gap between encoder and decoder, and also refines the shape of tooth prediction through a structural constraint loss. Experimental results suggest that ToothSegNet produces more precise segmentation and outperforms the state-of-the-art medical image segmentation methods.
- “A fully automatic ai system for tooth and alveolar bone segmentation from cone-beam ct images,” Nature communications, vol. 13, no. 1, pp. 1–11, 2022.
- “Cone beam computed tomography in oral and maxillofacial surgery: an evidence-based review,” Dentistry journal, vol. 7, no. 2, pp. 52, 2019.
- “Ai-enabled automatic multimodal fusion of cone-beam ct and intraoral scans for intelligent 3d tooth-bone reconstruction and clinical applications,” arXiv preprint arXiv:2203.05784, 2022.
- “Toothnet: automatic tooth instance segmentation and identification from cone beam ct images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6368–6377.
- “Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam ct,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020, pp. 939–942.
- “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
- “Hierarchical morphology-guided tooth instance segmentation from cbct images,” in International Conference on Information Processing in Medical Imaging. Springer, 2021, pp. 150–162.
- “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
- “Coarse-to-fine cnn for image super-resolution,” IEEE Transactions on Multimedia, vol. 23, pp. 1489–1502, 2020.
- “Effects of image degradation and degradation removal to cnn-based image classification,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 4, pp. 1239–1253, 2019.
- “Uctransnet: Rethinking the skip connections in u-net from a channel-wise perspective with transformer,” arXiv preprint arXiv:2109.04335, 2021.
- “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.
- “Image segmentation using deep learning: A survey,” IEEE transactions on pattern analysis and machine intelligence, 2021.
- “Unet++: A nested u-net architecture for medical image segmentation,” in Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer, 2018.
- “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440.
- “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587, 2017.
- “Medical transformer: Gated axial-attention for medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2021, pp. 36–46.
- “Mmsegmenation,” 2020.
- “Basnet: Boundary-aware salient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 7479–7489.