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BMC Oral Health
Published in: BMC Oral Health 1/2023

Open Access 01-12-2023 | Research

Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images

Authors: Yeon-Sun Yoo, DaEl Kim, Su Yang, Se-Ryong Kang, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Won-Jin Yi

Published in: BMC Oral Health | Issue 1/2023

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Abstract

Background

The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity.

Methods

The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS.

Results

The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net +  + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively.

Conclusions

The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.
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Metadata
Title
Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images
Authors
Yeon-Sun Yoo
DaEl Kim
Su Yang
Se-Ryong Kang
Jo-Eun Kim
Kyung-Hoe Huh
Sam-Sun Lee
Min-Suk Heo
Won-Jin Yi
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-023-03607-6

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