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https://www.um.edu.mt/library/oar/handle/123456789/148295| Title: | Automated tissue segmentation |
| Other Titles: | Dental clinical procedures using artificial intelligence |
| Authors: | Pozza, Mariana Balcewicz Han, Ji Yong Tran, Thi Ngoc Trang Akhmad, Rafik Abu-Gharbieh, Malek Schwitalla, Andreas Dominik Beuer, Florian Cortes, Arthur R. G. |
| Keywords: | Dentistry -- Data processing Artificial intelligence -- Medical applications Tissue engineering -- Data processing Image segmentation -- Data processing Dental implants -- Design and construction |
| Issue Date: | 2026 |
| Publisher: | Springer Nature |
| Citation: | Pozza, M. B., Han, J. Y., Tran, T. N. T., Akhmad, R., Abu-Gharbieh, M., Schwitalla, A. D.,…Cortes, A. R. G. (2026). Automated tissue segmentation. In A.R.G. Cortes (Ed.), Dental Clinical Procedures using Artificial Intelligence (pp. 187-214). Cham: Springer Nature Switzerland. |
| Abstract: | The creation of a virtual patient relies on the fusion of data from various imaging modalities, with cone-beam computed tomography (CBCT) serving as the primary source for detailed volumetric information on bone and tooth structures. The foundational step in building this digital representation of the patient is called image segmentation, which is the process of isolating and defining specific anatomical structures from the imaging data. However, manual tissue segmentation performed by a dental professional is often labor-intensive, time-consuming, and technically demanding. This chapter describes how AI has addressed these issues, particularly in the form of deep learning algorithms, by means of techniques of automated tissue segmentation. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/148295 |
| Appears in Collections: | Dental clinical procedures using artificial intelligence |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Automated tissue segmentation.pdf Restricted Access | 1.05 MB | Adobe PDF | View/Open Request a copy |
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