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Accuracy of ITK-SNAP software for 3D analysis of a non-regular topography structure

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Abstract

Objectives

To evaluate the accuracy of ITK-SNAP software for measuring volumes of a non-regular shape structure, using cone beam computed tomography (CBCT) scans, besides for developing a mathematical model to correct the software measurement error in case it existed.

Methods

A phantom made by moulding a rubber duck’s head was filled with total (38,000 mm3) and partial volumes of water (7000 mm3, 14,000 mm3, 21,000 mm3, 28,000 mm3 and 35,000 mm3), which constituted the gold standards. The sound phantom and the phantom filled with different volumes of water were scanned in a Picasso Trio CBCT unit set at 80 kVp, 3.7 mA, 0.2 mm3 voxel and 12 × 8.5 cm field of view. Semi-automatic segmentation was performed with ITK-SNAP 3.0 software by two trained oral radiologists. Linear regression analyzed the relation between ITK-SNAP calculated volumes and the gold standard. Intraclass correlation coefficient was applied to analyze the reproducibility of the method. Significance level was set at 5%.

Results

Linear regression analysis showed a significant relationship between ITK-SNAP volumes and the gold standard (F = 22,537.3, p < 0.0001), with an R2 of 0.9993. The average error found was 4.7 (± 4.3) %. To minimize this error, a mathematical model was developed and provided a reduction of it. ICC revealed excellent intra-examiner agreements for both examiners 1 (ICC = 0.9991, p < 0.0001) and 2 (ICC = 0.9989, p < 0.0001). Likewise, inter-examiner agreement was excellent (ICC = 0.9991, p < 0.0001).

Conclusion

The software showed to be accurate for evaluating non-regular shape structures. The mathematical model developed reduced an already small error on the software’s measurements.

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References

  1. Villoria EM, Lenzi AR, Soares RV, Souki BQ, Sigurdsson A, Marques AP, et al. Post-processing open-source software for the CBCT monitoring of periapical lesions healing following endodontic treatment: technical report of two cases. Dentomaxillofac Radiol. 2017;46:20160293.

    Article  Google Scholar 

  2. Vallaeys K, Kacem A, Legoux H, Le Tenier M, Hamitouche C, Arbab-Chirani R. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods. Dentomaxillofac Radiol. 2015;44:20150079.

    Article  Google Scholar 

  3. Bartikian M, Ferreira A, Gonçalves-Ferreira A, Neto LL. 3D printing anatomical models of head bones. Surg Radiol Anat. 2018. https://doi.org/10.1007/s00276-018-2148-4.

    Article  PubMed  Google Scholar 

  4. Weissheimer A, De Menezes LM, Sameshima GT, Enciso R, Pham J, Grauer D. Imaging software accuracy for 3-dimensional analysis of the upper airway. Am J Orthod Dentofac Orthop. 2012;142:801–13.

    Article  Google Scholar 

  5. Pinheiro ML, Yatabe M, Ioshida M, Orlandi L, Dumast PD, Trindade-Suedam IK. Volumetric reconstruction and determination of minimum cross-sectional area of the pharynx in patients with cleft lip and palate: comparison between two different softwares. J Appl Oral Sci. 2018;26:1–7.

    Article  Google Scholar 

  6. Brasil DM, Kurita LM, Groppo FC, Haiter-Neto F. Relationship of craniofacial morphology in 3-dimensional analysis of the pharynx. Am J Orthod Dentofac Orthop. 2016;149:683–91.

    Article  Google Scholar 

  7. Nejaim Y, Aps JKM, Groppo FC, Haiter Neto F. Evaluation of pharyngeal space and its correlation with mandible and hyoid bone in patients with different skeletal classes and facial types. Am J Orthod Dentofac Orthop. 2018;153:825–33.

    Article  Google Scholar 

  8. Grauer D, Cevidanes LSH, Styner MA, Ackerman JL, Proffit WR. Pharyngeal airway volume and shape from cone-beam computed tomography: relationship to facial morphology. Am J Orthod Dentofac Orthop. 2009;136:805–14.

    Article  Google Scholar 

  9. Gomes AF, Gamba TO, Yamasaki MC, Groppo FC, Haiter-Neto F, Possobon RF. Development and validation of a formula based on maxillary sinus measurements as a tool for sex estimation: a cone beam computed tomography study. Int J Legal Med. 2019;133(4):1241–49.

    Article  Google Scholar 

  10. Nejaim Y, Farias Gomes A, Valadares CV, Costa ED, Peroni LV, Groppo FC, et al. Evaluation of volume of the sphenoid sinus according to sex, facial type, skeletal class, and presence of a septum: a cone-beam computed tomographic study. Br J Oral Maxillofac Surg. 2019;57:336–40.

    Article  Google Scholar 

  11. Oliveira JM, Alonso MD, de Sousa ETMJ, Fuziy A, Scocate AC, Costa AL. Volumetric study of sphenoid sinuses: anatomical analysis in helical computed tomography. Surg Radiol Anat. 2017;39(4):367–74.

    Article  Google Scholar 

  12. Ge Z-P, Yang P, Li G, Zhang J, Ma X-C. Age estimation based on pulp cavity/chamber volume of 13 types of tooth from cone beam computed tomography images. Int J Legal Med. 2016;130:1159–67.

    Article  Google Scholar 

  13. Gomes AF, Nejaim Y, Brasil DM, Groppo FC, Ferreira Caria PH, Haiter Neto F. Assessment of volume and height of the coronoid process in patients with different facial types and skeletal classes: a cone-beam computed tomography study. J Oral Maxillofac Surg. 2015;73(1395):e1–e5.

    Google Scholar 

  14. Kirmeier R, Arnetzl C, Robl T, Payer M, Lorenzoni M, Jakse N. Reproducibility of volumetric measurements on maxillary sinuses. Int J Oral Maxillofac Surg. 2011;40:195–9.

    Article  Google Scholar 

  15. Shaheen E, Khalil W, Ezeldeen M, Van de Casteele E, Sun Y, Politis C, et al. Accuracy of segmentation of tooth structures using 3 different CBCT machines. Oral Surg Oral Med Oral Pathol Oral Radiol. 2017;123:123–8.

    Article  Google Scholar 

  16. Loubele M, Jacobs R, Maes F, Denis K, White S, Coudyzer W, et al. Image quality vs radiation dose of four cone beam computed tomography scanners. Dentomaxillofac Radiol. 2008;37:309–19.

    Article  Google Scholar 

  17. Holberg C, Steinhäuser S, Geis P, Rudzki-Janson I. Cone-beam computed tomography in orthodontics: benefits and limitations. J Orofac Orthop. 2005;66:434–44.

    Article  Google Scholar 

  18. Liu Y, Olszewski R, Alexandroni ES, Enciso R, Xu T, Mah JK. The validity of in vivo tooth volume determinations from cone-beam computed tomography. Angle Orthod. 2010;80:160–6.

    Article  Google Scholar 

  19. El H, Palomo JM. Measuring the airway in 3 dimensions: a reliability and accuracy study. Am J Orthod Dentofac Orthop. 2010;137:S50.e1–9 (discussion S50-S52).

    Google Scholar 

  20. Yushkevich PA, Yang Gao, Gerig G. ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: Conf. Proc. … Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2016. 2016. p. 3342–5.

  21. Pinheiro ML, Yatabe M, Ioshida M, Orlandi L, de Dumast P, Trindade-Suedam IK. Volumetric reconstruction and determination of minimum crosssectional area of the pharynx in patients with cleft lip and palate: comparison between two different softwares. J Appl Oral Sci. 2018;26:e20170282.

    Article  Google Scholar 

  22. Ge Z, Ma R, Li G, Zhang J, Ma X. Age estimation based on pulp chamber volume of first molars from cone-beam computed tomography images. Forensic Sci Int. 2015;253(133):e1–e7.

    Google Scholar 

  23. Khalil W, EzEldeen M, Van De Casteele E, Shaheen E, Sun Y, Shahbazian M, et al. Validation of cone beam computed tomography-based tooth printing using different three-dimensional printing technologies. Oral Surg Oral Med Oral Pathol Oral Radiol. 2016;121:307–15.

    Article  Google Scholar 

  24. Johnson HJ, Mccormick MM, IbáNez L, Insight Software Consortium. The ITK software guide book 1: introduction and development guidelines fourth edition updated for ITK version 4.13.0. 4a Edition. community@itk.org. 2017.

  25. Fyllingen EH, Stensjøen AL, Berntsen EM, Solheim O, Reinertsen I. Glioblastoma segmentation: comparison of three different software packages. PLoS ONE. 2016;11:e0164891.

    Article  Google Scholar 

  26. Lee D-K, Yoon U, Kwak K, Lee J-M. automated segmentation of cerebellum using brain mask and partial volume estimation map. Comput Math Methods Med. 2015;2015:167489.

    Article  Google Scholar 

  27. Prabhat M, Rai S, Kaur M, Prabhat K, Bhatnagar P, Panjwani S. Computed tomography based forensic gender determination by measuring the size and volume of the maxillary sinuses. J Forensic Dent Sci. 2016;8:40.

    Article  Google Scholar 

  28. Kim D-I, Lee U-Y, Park S-O, Kwak D-S, Han S-H. Identification using frontal sinus by three-dimensional reconstruction from computed tomography. J Forensic Sci. 2013;58:5–12.

    Article  Google Scholar 

  29. Farzal Z, Walsh J, Lopes-de-Rezende-Barbosa G, Zdanski CJ, Davis SD, Superfine R, et al. Volumetric nasal cavity analysis in children with unilateral and bilateral cleft lip and palate. Laryngoscope. 2016;126:1475–80.

    Article  Google Scholar 

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)–Finance Code 001.

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Correspondence to Danieli Moura Brasil.

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Gomes, A.F., Brasil, D.M., Silva, A.I.V. et al. Accuracy of ITK-SNAP software for 3D analysis of a non-regular topography structure. Oral Radiol 36, 183–189 (2020). https://doi.org/10.1007/s11282-019-00397-y

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