J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2021 , Vol. 26 >Issue 3: 298 - 305

DOI: https://doi.org/10.1007/s12204-021-2296-2

Medical 3D Printing and Personalised Medicine

 Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network

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  • (1. Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and
    Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Department of
    Second Dental Centre, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of
    Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for
    Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai 200011, China; 3. Institute of Medical Robotics,
    Shanghai Jiao Tong University, Shanghai 200240, China)

Online published: 2021-06-02

Supported by

the National Key Research and Development
Program of China (No. 2017YFB1302900),
the National Natural Science Foundation of China
(Nos. 81971709, M-0019, and 82011530141), the Foundation
of Science and Technology Commission of Shanghai
Municipality (Nos. 19510712200, and 20490740700),
and the Shanghai Jiao Tong University Foundation
on Medical and Technological Joint Science
Research (Nos. ZH2018ZDA15, YG2019ZDA06, and
ZH2018QNA23), and the 2020 Key Research Project of
Xiamen Municipal Government (No. 3502Z20201030)

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Abstract

Sinus floor elevation with a lateral window approach requires bone graft (BG) to ensure sufficient  bone mass, and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.  However, the BG region from cone-beam computed tomography (CBCT) images is connected to the margin of  the maxillary sinus, and its boundary is blurred. Common segmentation methods are usually performed manually  by experienced doctors, and are complicated by challenges such as low efficiency and low precision. In this study,  an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous  spatial pyramid convolution (ASPC) network. The ASPC module was adopted using residual connections to  compose multiple atrous convolutions, which could extract more features on multiple scales. Subsequently, a  segmentation network of the BG region with multiple ASPC modules was established, which effectively improved  the segmentation performance. Although the training data were insufficient, our networks still achieved good  auto-segmentation results, with a dice coefficient (Dice) of 87.13%, an Intersection over Union (Iou) of 78.01%,  and a sensitivity of 95.02%. Compared with other methods, our method achieved a better segmentation effect,  and effectively reduced the misjudgement of segmentation. Our method can thus be used to implement automatic  segmentation of the BG region and improve doctors’ work efficiency, which is of great importance for developing  preliminary studies on the measurement of postoperative BG within the maxillary sinus.   

Cite this article

XU Jiangchang (许江长), HE Shamin (何莎敏), YU Dedong (于德栋), WU Yiqun (吴轶群), CHEN Xiaojun, (陈晓军) .  Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network[J]. Journal of Shanghai Jiaotong University(Science), 2021 , 26(3) : 298 -305 . DOI: 10.1007/s12204-021-2296-2

References

[1] NIU L X, WANG J, YU H J, et al. New classification of maxillary sinus contours and its relation to sinus floor elevation surgery [J]. Clinical Implant Dentistry and Related Research, 2018, 20(4): 493-500.

[2] HUANG J, HU J H, LUO R C, et al. Linear measurements of sinus floor elevation based on voxel-based superimposition of cone beam computed tomography images [J]. Clinical Implant Dentistry and Related Research, 2019, 21(5): 1048-1053.

[3] GERRESSEN M, RIEDIGER D, HILGERS R D, et al. The volume behavior of autogenous iliac bone grafts after sinus floor elevation: A clinical pilot study [J]. The Journal of Oral Implantology, 2015, 41(3): 276- 283.

[4] KIRMEIER R, PAYER M, WEHRSCHUETZ M, et al. Evaluation of three-dimensional changes after sinus floor augmentation with different grafting materials [J]. Clinical Oral Implants Research, 2008, 19(4): 366-372.

[5] OKADA T, KANAI T, TACHIKAWA N, et al. Longterm radiographic assessment of maxillary sinus floor augmentation using beta-tricalcium phosphate: Analysis by cone-beam computed tomography [J]. International Journal of Implant Dentistry, 2016, 2(1): 1-9.

[6] BERBERI A, BOUSERHAL L, NADER N, et al. Evaluation of three-dimensional volumetric changes after sinus floor augmentation with mineralized cortical bone allograft [J]. Journal of Maxillofacial and Oral Surgery, 2015, 14(3): 624-629.

[7] MAZZOCCO F, LOPS D, GOBBATO L, et al. Threedimensional volume change of grafted bone in the maxillary sinus [J]. The International Journal of Oral & Maxillofacial Implants, 2014, 29(1): 178-184.

[8] GULTEKIN B A, BORAHAN O, SIRALI A, et al. Three-dimensional assessment of volumetric changes in sinuses augmented with two different bone substitutes [J]. BioMed Research International, 2016, 2016: 4085079.

[9] LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis [J]. Medical Image Analysis, 2017, 42: 60-88.

[10] CHARTRAND G, CHENG P M, VORONTSOV E, et al. Deep learning: A primer for radiologists [J]. Radio- Graphics, 2017, 37(7): 2113-2131.

[11] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

[12] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation [C]//Proceedings of the International Conference on Medical Image Computing and Computer- Assisted Intervention. Cham: Springer, 2015: 234-241.

[13] MILLETARI F, NAVAB N, AHMADI S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation [C]//Proceedings of the IEEE Fourth International Conference on 3D Vision. Piscataway: IEEE, 2016: 565-571.

[14] QIU B J, GUO J P, KRAEIMA J, et al. Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network [J]. Physics in Medicine and Biology, 2019, 64(17): 175020.

[15] CUI Z M, LI C J, WANG W P. ToothNet: automatic tooth instance segmentation and identification from cone beam CT images [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2019: 6358-6377.

[16] CHEN F, LIU J, ZHAO Z, et al. Three-dimensional feature-enhanced network for automatic femur segmentation [J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 243-252.

[17] AMBELLAN F, TACK A, EHLKE M, et al. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative [J]. Medical Image Analysis, 2019, 52: 109-118.

[18] ZHANG J, LIU M X, WANG L, et al. Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization [J]. Medical Image Analysis, 2020, 60: 101621.

[19] XU J C, WANG S M, ZHOU Z J, et al. Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net [J]. International Journal of Computer Assisted Radiology and Surgery, 2020, 15(9): 1457-1465.

[20] YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions [EB/OL]. (2016-04-30) [2020- 08-10]. https://arxiv.org/pdf/1511.07122.pdf.

[21] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[22] GU Z W, CHENG J, FU H Z, et al. CE-Net: Context encoder network for 2D medical image segmentation [J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292. [23] LI Q F, SHEN L L. 3D neuron reconstruction in tangled neuronal image with deep networks [J]. IEEE Transactions on Medical Imaging, 2020, 39(2): 425- 435.
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