Reinforcement Learning Based Vocal Fold Localization in Preoperative Neck CT for Injection Laryngoplasty
نویسندگان
چکیده
Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material to it. Hence, localization plays vital role in the preoperative planning, as location required determine optimal route. In this communication, we propose mirror environment based reinforcement learning (RL) algorithm localizing right and left folds neck CT. RL-based methods commonly showed noteworthy outcomes general anatomic landmark problems recent years. However, such suggest training individual agents each fold, although are located close proximity have high feature-similarity. Utilizing lateral symmetry between folds, proposed allows single agent both flipped version of fold. Thus, can be trained using session that utilizes inter-fold correlation avoids redundant feature learning. Experiments with 120 CT volumes improved performance efficiency method compared standard RL method.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13010262