Three-dimensional soft tissue landmark detection with marching cube algorithm

Yoonjung Lee, Ji Min Lee, Sun Hyung Park, Yoon Jeong Choi, Sung Hwan Choi, Jae Joon Hwang, Hyung Seog Yu

Research output: Contribution to journalArticlepeer-review

Abstract

Current method of analyzing three-dimensional soft tissue data, especially in the frontal view, is subjective and has poor reliability. To overcome this limitation, the present study aimed to introduce a new method of analyzing soft tissue data reconstructed by marching cube algorithm (Program S) and compare it with a commercially available program (Program A). Cone-beam computed tomography images of 42 patients were included. Two orthodontists digitized six landmarks (pronasale, columella, upper and lower lip, right and left cheek) twice using both programs in two-week intervals, and the reliability was compared. Furthermore, computer-calculated point (CC point) was developed to evaluate whether human error could be reduced. The results showed that the intra- and inter-examiner reliability of Program S (99.7–100% and 99.9–100%, respectively) were higher than that of Program A (64.0–99.9% and 76.1–99.9%, respectively). Moreover, the inter-examiner difference of coordinate values and distances for all six landmarks in Program S was lower than Program A. Lastly, CC point was provided as a consistent single point. Therefore, it was validated that this new methodology can increase the intra- and inter-examiner reliability of soft tissue landmark digitation and CC point can be used as a landmark to reduce human error.

Original languageEnglish
Article number1544
JournalScientific reports
Volume13
Issue number1
DOIs
Publication statusPublished - 2023 Dec

Bibliographical note

Funding Information:
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI20C0611).

Publisher Copyright:
© 2023, The Author(s).

All Science Journal Classification (ASJC) codes

  • General

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