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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep
Yiming Sun1, Xingru Huang2, Qianni Zhang2
1Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
Purpose:
To automatically predict the postoperative appearance of blepharoptosis surgeries and evaluate the generated images both objectively and subjectively in a clinical setting.
Design:
Cross-sectional study.
Participants:
This study involved 970 pairs of images of 450 eyes from 362 patients undergoing blepharoptosis surgeries at our oculoplastic clinic between June 2016 and April 2021.
Methods:
Preoperative and postoperative facial images were used to train and test the deep learning-based postoperative appearance prediction system (POAP) consisting of 4 modules, including the data processing module (P), ocular detection module (O), analyzing module (A), and prediction module (P).
Main Outcome Measures:
The overall and local performance of the system were automatically quantified by the overlap ratio of eyes and by lid contour analysis using midpupil lid distances (MPLDs). Four ophthalmologists and 6 patients were invited to complete a satisfaction scale and a similarity survey with the test set of 75 pairs of images on each scale.
Results:
The overall performance (mean overlap ratio) was 0.858 ± 0.082. The corresponding multiple radial MPLDs showed no significant differences between the predictive results and the real samples at any angle (P > 0.05). The absolute error between the predicted marginal reflex distance-1 (MRD1) and the actual postoperative MRD1 ranged from 0.013 mm to 1.900 mm (95% within 1 mm, 80% within 0.75 mm). The participating experts and patients were "satisfied" with 268 pairs (35.7%) and "highly satisfied" with most of the outcomes (420 pairs, 56.0%). The similarity score was 9.43 ± 0.79.
Conclusions:
The fully automatic deep learning-based method can predict postoperative appearance for blepharoptosis surgery with high accuracy and satisfaction, thus offering the patients with blepharoptosis an opportunity to understand the expected change more clearly and to relieve anxiety. In addition, this system could be used to assist patients in selecting surgeons and the recovery phase of daily living, which may offer guidance for inexperienced surgeons as well.
