  Precision tooth segmentation is crucial in the oral sector because it
provides location information for orthodontic therapy, clinical diagnosis, and
surgical treatments. In this paper, we investigate residual, recurrent, and
attention networks to segment teeth from panoramic dental images. Based on our
findings, we suggest three single-stage models: Single Recurrent R2U-Net
(S-R2U-Net), Single Recurrent Filter Double R2U-Net (S-R2F2U-Net), and Single
Recurrent Attention Enabled Filter Double (S-R2F2-Attn-U-Net). Particularly,
S-R2F2U-Net outperforms state-of-the-art models in terms of accuracy and dice
score. A hybrid loss function combining the cross-entropy loss and dice loss is
used to train the model. In addition, it reduces around 45% of model parameters
compared to the R2U-Net model. Models are trained and evaluated on a benchmark
dataset containing 1500 dental panoramic X-ray images. S-R2F2U-Net achieves
97.31% of accuracy and 93.26% of dice score, showing superiority over the
state-of-the-art methods. Codes are available at
https://github.com/mrinal054/teethSeg_sr2f2u-net.git.
