TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network

Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

“…In the study of identifying tooth position, there is also a study by H. Chen et al [8] using Fast-RCNN and three post-processing methods to automatically distinguish the area where the tooth is located and label the tooth position. are also conducting similar research on DPR identification or segmentation, which not only shows that DPR has gradually become the main in dicator for judging dental conditions, it also shows automatic identification of DPR has become the current major trend [9], [10], [11], [12], [13]. The automatic image recognition of DPR has not only become the current major trend, but also reduces the time for manual inspection of DPR images.…”

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

“…The initial results obtained were 84% precision, 76% recall and 92% accuracy. Subsequently, using this same database, the authors in [5] proposed the TSASNet (two-stage attention segmentation network) which is divided into two stages. The first stage contains an attention network, which can be global and/or local, to obtain preliminary information on the radiograph and, in the second stage, in fact, a segmentation network.…”

“…Other models using ensemble strategies were proposed, in which the best model obtained results of 93.6%, 93.3% and 94.3% of Dice, precision and recall, respectively. This work is very similar to the works of [5,17], but it uses other segmentation models that are gaining prominence in the literature with other medical segmentation datasets [18][19][20][21].…”

“…This process, called dental segmentation, is of paramount importance for the recognition of the visual dental pattern [4]. Today, dental segmentation plays a crucial role in supporting diagnosis and treatment plans [5].…”

“…In [14], deep learning was used to segment the mandibular canal using computed tomography volumes. While in [5], segmentation of teeth in panoramic radiographs was performed. Although the results of previous research on AI have been extremely promising, the studies are still preliminary.…”