Automated methods for sella turcica segmentation on cephalometric radiographic data using deep learning (CNN) techniques.

objectiveThe objective of this work is to present a novel technique using convolutional neural network (CNN) architectures for automatic segmentation of sella turcica (ST) on cephalometric radiographic image dataset. The proposed work suggests possible deep learning approaches to distinguish ST on complex cephalometric radiographs using deep learning techniques. MATERIALS AND

methodsThe dataset of 525 lateral cephalometric images was employed and randomly split into different training and testing subset ratios. The ground truth (annotated images) represents pixel-wise annotation of the ST using an online annotation platform by dental specialists. This study compared convolutional neural network architectures based on fine-tuned versions of the VGG19, ResNet34, InceptionV3, and ResNext50 architectures to select an appropriate model for autonomous segmentation of the nonlinear structure of ST.

resultsThe study compared training and prediction results of the selected models: VGG19, ResNet34, InceptionV3, and ResNext50. The mean IoU scores for VGG19, ResNet34, InceptionV3 and ResNext50 are 0.7651, 0.7241, 0.4717, 0.4287, dice coefficients are 0.7794, 0.7487, 0.4714, 0.4363 and loss scores are 0.0973, 0.1299, 0.2049 and 0.2251, respectively.

conclusionThe obtained findings suggest that the VGG19 and Resnet34 architectures (mean IoU and dice coefficient > 75%) comparatively outperformed the InceptionV3 and ResNext50 architectures (mean IoU and dice coefficients is around 45%) for considered cephalometric radiographic dataset. The study findings can be used as a reference model for future investigation of non-linear ST morphological characteristics and related biological anomalies.