A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs

Abstract

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

Figure 1

Annotation of the apical lesion using polygonal box method.

Figure 2

The U-Net architecture for the semantic segmentation task.

Figure 3

Model pipeline for apical lesion segmentation (CranioCatch, Eskisehir, Turkey).

Figure 4

Automatically apical lesion segmentation using AI model (CranioCatch, Eskisehir, Turkey).

Figure 5

An example real-prediction image comparison.