Automated Zebrafish Spine Scoring System Based on Instance Segmentation
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,...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10849560/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576785753047040 |
|---|---|
| author | Wen-Hsin Chen Tien-Ying Kuo Yu-Jen Wei Cheng-Jung Ho Ming-der Lin Huan Chen Wen-Ying Lin |
| author_facet | Wen-Hsin Chen Tien-Ying Kuo Yu-Jen Wei Cheng-Jung Ho Ming-der Lin Huan Chen Wen-Ying Lin |
| author_sort | Wen-Hsin Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cc3b71e6833441ea97a4d5e1ee5f8ba4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cc3b71e6833441ea97a4d5e1ee5f8ba42025-01-31T00:00:47ZengIEEEIEEE Access2169-35362025-01-0113188141882610.1109/ACCESS.2025.353268010849560Automated Zebrafish Spine Scoring System Based on Instance SegmentationWen-Hsin Chen0Tien-Ying Kuo1https://orcid.org/0000-0001-9831-5622Yu-Jen Wei2Cheng-Jung Ho3Ming-der Lin4Huan Chen5https://orcid.org/0000-0003-0410-3843Wen-Ying Lin6Department of Electrical Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Molecular Biology and Human Genetics, Tzu Chi University, Hualien, TaiwanDepartment of Computer Science and Engineering, National Chung Hsing University, Taichung City, TaiwanDepartment of Molecular Biology and Human Genetics, Tzu Chi University, Hualien, TaiwanIn 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.https://ieeexplore.ieee.org/document/10849560/Deep learningmachine learningobject segmentationimage analysis |
| spellingShingle | Wen-Hsin Chen Tien-Ying Kuo Yu-Jen Wei Cheng-Jung Ho Ming-der Lin Huan Chen Wen-Ying Lin Automated Zebrafish Spine Scoring System Based on Instance Segmentation IEEE Access Deep learning machine learning object segmentation image analysis |
| title | Automated Zebrafish Spine Scoring System Based on Instance Segmentation |
| title_full | Automated Zebrafish Spine Scoring System Based on Instance Segmentation |
| title_fullStr | Automated Zebrafish Spine Scoring System Based on Instance Segmentation |
| title_full_unstemmed | Automated Zebrafish Spine Scoring System Based on Instance Segmentation |
| title_short | Automated Zebrafish Spine Scoring System Based on Instance Segmentation |
| title_sort | automated zebrafish spine scoring system based on instance segmentation |
| topic | Deep learning machine learning object segmentation image analysis |
| url | https://ieeexplore.ieee.org/document/10849560/ |
| work_keys_str_mv | AT wenhsinchen automatedzebrafishspinescoringsystembasedoninstancesegmentation AT tienyingkuo automatedzebrafishspinescoringsystembasedoninstancesegmentation AT yujenwei automatedzebrafishspinescoringsystembasedoninstancesegmentation AT chengjungho automatedzebrafishspinescoringsystembasedoninstancesegmentation AT mingderlin automatedzebrafishspinescoringsystembasedoninstancesegmentation AT huanchen automatedzebrafishspinescoringsystembasedoninstancesegmentation AT wenyinglin automatedzebrafishspinescoringsystembasedoninstancesegmentation |