Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN

Abstract

BACKGROUND:

Automatic segmentation of individual tooth root is a key technology for the reconstruction of the three-dimensional dental model from Cone Beam Computed Tomography (CBCT) images, which is of great significance for the orthodontic, implant and other dental diagnosis and treatment planning.

OBJECTIVES:

Currently, tooth root segmentation is mainly done manually because of the similar gray of the tooth root and the alveolar bone from CBCT images. This study aims to explore the automatic tooth root segmentation algorithm of CBCT axial image sequence based on deep learning.

METHODS:

We proposed a new automatic tooth root segmentation method based on the deep learning U-net with AGs. Since CBCT sequence has a strong correlation between adjacent slices, a Recurrent neural network (RNN) was applied to extract the intra-slice and inter-slice contexts. To develop and test this new method for automatic segmentation of tooth roots using CBCT images, 24 sets of CBCT sequences containing 1160 images and 5 sets of CBCT sequences containing 361 images were used to train and test the network, respectively.

RESULTS:

Applying to the testing dataset, the segmentation accuracy measured by the intersection over union (IOU), dice similarity coefficient (DICE), average precision rate (APR), average recall rate (ARR), and average symmetrical surface distance (ASSD) are 0.914, 0.955, 95.8% , 95.3% , 0.145 mm, respectively.

CONCLUSIONS:

The study demonstrates that the new method combining attention U-net with RNN yields the promising results of automatic tooth roots segmentation, which has potential to help improve the segmentation efficiency and accuracy in future clinical practice.

Keywords

Tooth roots automatic segmentation cone beam computed tomography attention U-net RNN

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References

Guerrero

M.E.

, Noriega

, Castro

, Jacobs

, Does cone-beam CT alter treatment plans? Comparison of preoperative implant planning using panoramic versus cone-beam CT images, Imaging Science in Dentistry 44(2) (2014), 121–128.

Sherrard

J.F.

, Rossouw

P.E.

, Benson

B.W.

, et al., Accuracy and reliability of tooth and root lengths measured on cone-beam computed tomographs, Am J Orthod Dentofacial Orthop 137(4 Suppl) (2010), S100–S108.

Watanabe

, Deguchi

, Hasegawa

, et al., Orthodontic miniscrew failure rate and root proximity, insertion angle, bone contact length, and bone density, Orthod Craniofac Res 16(1) (2013), 44–55.

Kuroda

, Yamada

, Deguchi

, et al., Root proximity is a major factor for screw failure in orthodontic anchorage, Am J Orthod Dentofacial Orthop 131(4 Suppl) (2007), S68–S73.

Yoon

W.J.

, Kim

S.G.

, Jeong

M.A.

, et al., Prognosis and evaluation of tooth damage caused by implant fixtures, Journal of the Korean Association of Oral and Maxillofacial Surgeons 39(3) (2013), 144–147.

Nishioka

, Ioi

, Nakata

, et al., Root Resorption and Immune System Factors in the Japanese, The Angle Orthodontist 76(1) (2006), 103–108.

Sameshima

G.T.

, Sinclair

P.M.

, Predicting and preventing root resorption: Part I. Diagnostic factors, Am J Orthod Dentofacial Orthop 119(5) (2001), 505–510.

Gao

, Chae

, Individual tooth segmentation from CT images using level set method with shape and intensity prior, Pattern Recognit 43(7) (2010), 2406–2417.

Gan

, Xia

, Xiong

, et al., Towards accurate tooth segmentation from computed tomography images using a hybrid level set model, Med Phys 42(1) (2015), 14–27.

10.

Gan

, Xia

, Xiong

, et al., Tooth and alveolar bone segmentation from dental computed tomography images, IEEE Journal of Biomedical and Health Informatics 22(1) (2018), 196–204.

11.

Wang

, Liu

, Wang

, Liu

, Accurate tooth segmentation with improved hybrid active contour model, Phys Med Biol 64(1) (2019), 015012.

12.

Kakehbaraei

, Seyedarabi

, Dental segmentation in cone– beam computed tomography images using watershed and morphology operators, Journal of Medical Signals and Sensors 8(2) (2018), 119–124.

13.

Tan

, Liang

, Li

, et al., Automatic prostate segmentation based on fusion between deep network and variational methods, J Xray Sci Technol 27(5) (2019), 821–837.

14.

Yang

, Song

, Li

, Tang

, Improving brain tumor segmentation on MRI based on the deep U-net and residual units, J Xray Sci Technol 28(1) (2020), 95–110.

15.

, Yang

, Automatic dental root CBCT image segmentation based on CNN and level set method, Proc SPIE 10949 (2019), 109492N.

16.

Gou

, Rao

, Zhang

, et al., Automatic Image Annotation and Deep Learning for Tooth CT Image Segmentation, International Conference on Image and Graphics (2019), 519–528.

17.

Macho

, Kurz

, Ulges

, et al., Segmenting teeth from volumetric CT data with a hierarchical., CGVC ’18 Proceedings of the Conference on Computer Graphics & Visual Computing (2018), 109–113.

18.

Ezhov

, Zakirov

, Gusarev

, Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT, 2019 IEEE 16th International Symposium on Biomedical Imaging (2019), 52–56.

19.

Oktay

O.S.

, Jo

, Loic

, et al., Attention U-Net: learning where to look for the pancreas, 1st Conference on Medical Imaging with Deep Learning (2018)

20.

Ronneberger

, Fischer

, Brox

, U-Net: Convolutional networks for biomedical image segmentation.MICCAI , Medical Image Computing and Computer-Assisted Intervention 9351 (2015), 234–241.

21.

Hochreiter

, Schmidhuber

, Long short-term memory, Neural Computation 9(8) (1997), 1735–1780.

22.

Shi

, Chen

, Wang

, et al., Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Advances in Neural Information Processing Systems 28 (2015).

23.

Chen

J.X

, Yang

, Zhang

Y.Z.

, et al., Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation, Advances in Neural Information Processing System 29 (2016).