版权所有:内蒙古大学图书馆 技术提供:维普资讯• 智图
内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Tzu Chi Univ Dept Mol Biol & Human Genet Hualien 97004 Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40202 Taiwan
出 版 物:《IEEE ACCESS》 (IEEE Access)
年 卷 期:2025年第13卷
页 面:18814-18826页
核心收录:
基 金:National Science and Technology Council [112-2221-E-027-083, 113-2221-E-027-081-MY2] Tzu Chi University [610400239-11]
主 题:Feature extraction Accuracy Micromechanical devices Classification algorithms Object recognition Instance segmentation Deep learning Computer architecture Proposals Prediction algorithms machine learning object segmentation image analysis
摘 要:In studying new medicines for osteoporosis, researchers use zebrafish as animal subjects to test drugs and observe the growth situation of their vertebrae in the spine to confirm the efficacy of new medicines. However, the current method for evaluating efficacy is time-consuming and labor-intensive, requiring manual observation. Taking advantage of advancements in deep learning technology, we propose an automatic method for detecting and recognizing zebrafish vertebrae of the images captured from image sensors to solve this problem. Our method was designed using Mask R-CNN as the instance segmentation backbone, enhanced with a mask enhancement module and a small object preprocessing approach to strengthen its detection abilities. Compared to the original Mask R-CNN architecture, our method improved the mean average precision (mAP) score for vertebra bounding box and mask detection by 7.1% to 97.7% and by 1.2% to 96.6%, respectively. Additionally, we developed a system using these detection algorithms to automatically calculate spinal vertebra growth scores, providing a valuable tool for researchers to assess drug efficacy.