Semantic segmentation of mouse jaws using convolutional neural networks

Cooley, Victoria; Stock, Stuart R.; Guise, William E.; Verma, Adya; Wald, Tomáš; Klein, Ophir D.; Joester, Derk

doi:10.1117/12.2594332

Cited by 2 publications

References 11 publications

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Adapting neural networks for rapid segmentation of mineralized tissues in mouse jaws

Cooley,

Suwandi,

Stock

et al. 2024

Developments in X-Ray Tomography XV

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Congenital defects in dental enamel are diverse in pathology and etiology, and designing treatment tools for the clinic requires fundamental research on the process of enamel formation. Rodent incisors are the model of choice, and microcomputed tomography (μCT) is often the first method of comparison between models. Quantitative comparison of μCT data requires segmentation of mineralized tissues in the jaw; previously, we demonstrated the ability of convolutional neural networks to quickly and accurately segment mineral gradients in mouse jaws in synchrotron μCT images. Here we greatly expand on that work and present a protocol for adapting base networks to new pathologies and data types. With collaborators, we have amassed a collection (~80 TB) of μCT images from laboratory machines and synchrotrons representing 18 genetic mouse lines. We demonstrate the ability of adapted networks to segment these new data without compromising accuracy. Specifically, our networks adapted well to data collected with different x-ray sources, voxel dimensions, and phenotypes. In fully segmented data, we demonstrate the ability to visualize stages during enamel formation and compare rates of change in mineral density during the process. Importantly, our work has revealed insights about how and when mineral deposition goes awry in defective enamel. We envision widespread use of these tools. Once base networks are deployed to a repository for artificial neural networks, researchers will be able to use the protocol we present here for using modest amounts of their data to adapt a network to their own analysis.

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Adapting neural networks for rapid segmentation of mineralized tissues in mouse jaws

Cooley,

Suwandi,

Stock

et al. 2024

Developments in X-Ray Tomography XV

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Automatic Segmentation of the Jaws Used in Guided Insertion of Orthodontic Mini Implants to Improve Their Stability and Precision

David,

Brad,

Rusu

et al. 2024

Medicina

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Background and Objectives: With the goal of identifying regions with bicortical bone and avoiding root contact, the present study proposes an innovative technique for the simulation of the insertion of mini orthodontic implants using automatic jaw segmentation. The simulation of mini implants takes place in 3D rendering visualization instead of Multi-Planar Reconstruction (MPR) sections. Materials and Methods: The procedure involves utilizing software that automatically segments the jaw, teeth, and implants, ensuring their visibility in 3D rendering images. These segmented files are utilized as study models to determine the optimum location for simulating orthodontic implants, in particular locations characterized by limited distances between the implant and the roots, as well as locations where the bicortical structures are present. Results: By using this method, we were able to simulate the insertion of mini implants in the maxilla by applying two cumulative requirements: the implant tip needs to be positioned in a bicortical area, and it needs to be situated more than 0.6 mm away from the neighboring teeth’s roots along all of their axes. Additionally, it is possible to replicate the positioning of the mini implant in order to distalize the molars in the mandible while avoiding the mandibular canal and the path of molar migration. Conclusions: The utilization of automated segmentation and visualization techniques in 3D rendering enhances safety measures during the simulation and insertion of orthodontic mini implants, increasing the insertion precision and providing an advantage in the identification of bicortical structures, increasing their stability.

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Semantic segmentation of mouse jaws using convolutional neural networks

Cited by 2 publications

References 11 publications

Adapting neural networks for rapid segmentation of mineralized tissues in mouse jaws

Adapting neural networks for rapid segmentation of mineralized tissues in mouse jaws

Automatic Segmentation of the Jaws Used in Guided Insertion of Orthodontic Mini Implants to Improve Their Stability and Precision

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