{Reference Type}: Journal Article
{Title}: Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images.
{Author}: Nogueira-Reis F;Morgan N;Nomidis S;Van Gerven A;Oliveira-Santos N;Jacobs R;Tabchoury CPM;
{Journal}: Clin Oral Investig
{Volume}: 27
{Issue}: 3
{Year}: Mar 2023
{Factor}: 3.606
{DOI}: 10.1007/s00784-022-04708-2
{Abstract}: <B>OBJECTIVE: </B>To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images.<BR><B>METHODS: </B>A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated.<BR><B>RESULTS: </B>From the dataset, 85% scored 7-10, and 15% were within 3-6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm.<BR><B>CONCLUSIONS: </B>The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP.<BR><B>CONCLUSIONS: </B>The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.