PloS one · 2016
Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.
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Jañez-Garcia Lucia, Saenz-Frances Federico, Ramirez-Sebastian Jose M, Toledano-Fernandez Nicolas, Urbasos-Pascual Maria, Jañez-Escalada Luis
Аннотация
OBJECTIVE: To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC) from CT scans whereby the size and main morphometric characteristics of the canal can be determined.
DESIGN: Cross-sectional study.
SUBJECTS: 36 eyes of 18 healthy individuals.
METHODS: Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) were established.
MAIN OUTCOME MEASURES: Time for segmentation, NLC volume, axis, sectional areas and length.
RESULTS: Mean processing time was around 30 seconds for segmenting each canal. All the variables generated were normally distributed. Measurements obtained using the four models polygonal, 2nd, 3rd and 4th degree polynomial, respectively, were: mean canal length 14.74, 14.3, 14.80, and 15.03 mm; mean sectional area 15.15, 11.77, 11.43, and 11.56 mm2; minimum sectional area 8.69, 7.62, 7.40, and 7.19 mm2; and mean depth of minimum sectional area (craniocaudal) 7.85, 7.71, 8.19, and 8.08 mm.
CONCLUSION: The method proposed automatically reconstructs the NLC on CT scans. Using these reconstructions, morphometric measurements can be calculated from NLC axis estimates based on polygonal and 2nd, 3rd and 4th polynomial models.
DESIGN: Cross-sectional study.
SUBJECTS: 36 eyes of 18 healthy individuals.
METHODS: Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) were established.
MAIN OUTCOME MEASURES: Time for segmentation, NLC volume, axis, sectional areas and length.
RESULTS: Mean processing time was around 30 seconds for segmenting each canal. All the variables generated were normally distributed. Measurements obtained using the four models polygonal, 2nd, 3rd and 4th degree polynomial, respectively, were: mean canal length 14.74, 14.3, 14.80, and 15.03 mm; mean sectional area 15.15, 11.77, 11.43, and 11.56 mm2; minimum sectional area 8.69, 7.62, 7.40, and 7.19 mm2; and mean depth of minimum sectional area (craniocaudal) 7.85, 7.71, 8.19, and 8.08 mm.
CONCLUSION: The method proposed automatically reconstructs the NLC on CT scans. Using these reconstructions, morphometric measurements can be calculated from NLC axis estimates based on polygonal and 2nd, 3rd and 4th polynomial models.