# Robust Segmentation of Partial and Imperfect Dental Arches
## 4 Results and Discussion
### Potential Applications and Limitations  
The proposed dental arch segmentation framework is valuable for treatment diagnosis and treatment planning [22; 45]. In addition, with its ability to process dental preparations, the proposed framework is able to provide the neighbouring context required for dental crown generation. During the training of a crown generation deep learning model [32], the prepared tooth, adjacent teeth on the master arch, and opposing teeth on the antagonist arch should be segmented to provide the necessary context. Dental laboratories usually provide the die mesh for each preparation, which could also be used to enhance the segmentation process. For each case in our dataset, the information describing the labels' range, missing teeth label(s), preparation label, and labels of teeth with incomplete side were provided. We used this information to select specific test cases for ArchSeg evaluation such that the a wide range of imperfections are present in the test cases.  
When available, the die mesh was used for lingual/buccal identification during master arch registration. In most cases, dental laboratories provide aligned scans of both the master and antagonist arches. Therefore, in such cases, the transformation matrix used to register the master arch was also used to register the antagonist arch. The die mesh was also helpful in post-processing and preparation repairing because the scan of the master arch might not contain all preparation details. In the situation when the model predicted a wrong label for the prepared tooth, the die mesh was used to correct the label in the arch because the die's label is known.  
The proposed dental arch segmentation framework, ArchSeg, also presents a few limitations: (1) To segment partial arches, ArchSeg relies on the information provided by user such as the range of teeth labels in the arch and the availability of die meshes to assist arch registration; (2) The registration of partial arches is not robust enough when the ambiguity of lingual/buccal exists; (3) The presence of extreme cases such as missing teeth without space is challenging; (4) Incomplete side teeth of an arch may be mislabeled or labeled as gingiva.  
The solution proposed is aimed to assist dental technicians in automated and accurate segmentation of teeth. As such, this tool could be integrated into an application programming interface (API) that allows the user to modify and download the output. In addition, this segmentation tool has been deployed at [[https://app.intellidentia.com/](https://app.intellidentia.com/)]([https://app.intellidentia.com/](https://app.intellidentia.com/)). It has been validated by collaborating dental professionals and pending further evaluations by external experts and users. Future work will focus on the creation of synthetic data covering cases of missing teeth without space as well as adding more data from different dental laboratories to enhance the generalization of the segmentation framework. Note that our ArchSeg framework is designed to allow the integration of any segmentation model in future. The arch segmentation model can be chosen by advanced users using the framework at [[https://app.intellidentia.com/](https://app.intellidentia.com/)]([https://app.intellidentia.com/](https://app.intellidentia.com/)).