Shanghai kou qiang yi xue = Shanghai journal of stomatology · 2025
[Segmentation and validation of mandibular canal and its bifurcation on cone beam CT based on deep learning].
СтатьяВалидационное исследованиеАннотация на английскомНазвание и аннотация: Перевод пока не подготовлен
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Ye Ye, Fang Shuobo, Lu Huitong, Liu Mingqian, Wu Xueying
Аннотация
PURPOSE: To train the U-net of convolutional neural network to establish a method for detecting and segmenting the mandibular canal and its bifurcation, and validate its accuracy based on the ground truth labeled by experts.
METHODS: A total of 290 CBCT scans were collected from Shanghai Stomatological Hospital from Jan. 2022 to Dec. 2022, which were divided into training set of 200 scans and test set of 90 scans. Model training included two steps. In the first step, bilateral mandibular canals and its bifurcation of 50 CBCT scans were labeled in 3D Slicer image computing platform by investigators. Three dimensional U-net segmentation model were trained initially with data enhancement. A morphological post-processing method was applied to the predicted results. In the second step, pseudo label method was employed to help annotating the mandibular canal and corresponding bifurcations on remaining 150 CBCTs, which would be included in training set after revision. Three dimensional U-net model was trained based on these 200 data. During test phase, totally 90 scans were labeled by two doctors and U-net model respectively. Consistency check was conducted to evaluate the labels between two doctors. Dice similarity coefficient and Hausdorff distance were calculated to evaluate the labels between doctors and the model. The detection rate of bifurcation was calculated. SPSS 20.0 software package was used for data analysis.
RESULTS: In 90 CBCT test set, the Kappa value between two dentists' annotations was 0.667. The average Dice and Hausdorff distance between predictions and labels of doctors were (0.739±0.068) and (0.988±1.14) mm. In bifurcation detection, the detection rate was 91.30% on scans with clear bifurcations.
CONCLSIONS: The dentification and segmentation U-net model of mandibular canal on dental CBCT can be reliable and practical for its high segmentation precision and predicting speed.
METHODS: A total of 290 CBCT scans were collected from Shanghai Stomatological Hospital from Jan. 2022 to Dec. 2022, which were divided into training set of 200 scans and test set of 90 scans. Model training included two steps. In the first step, bilateral mandibular canals and its bifurcation of 50 CBCT scans were labeled in 3D Slicer image computing platform by investigators. Three dimensional U-net segmentation model were trained initially with data enhancement. A morphological post-processing method was applied to the predicted results. In the second step, pseudo label method was employed to help annotating the mandibular canal and corresponding bifurcations on remaining 150 CBCTs, which would be included in training set after revision. Three dimensional U-net model was trained based on these 200 data. During test phase, totally 90 scans were labeled by two doctors and U-net model respectively. Consistency check was conducted to evaluate the labels between two doctors. Dice similarity coefficient and Hausdorff distance were calculated to evaluate the labels between doctors and the model. The detection rate of bifurcation was calculated. SPSS 20.0 software package was used for data analysis.
RESULTS: In 90 CBCT test set, the Kappa value between two dentists' annotations was 0.667. The average Dice and Hausdorff distance between predictions and labels of doctors were (0.739±0.068) and (0.988±1.14) mm. In bifurcation detection, the detection rate was 91.30% on scans with clear bifurcations.
CONCLSIONS: The dentification and segmentation U-net model of mandibular canal on dental CBCT can be reliable and practical for its high segmentation precision and predicting speed.