Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images
Purpose: Incorrect patient positioning during radiotherapy can significantly impact treatment efficacy and pose potential risks. This study aims to develop a model that can rapidly and effectively monitor the patient’s postures during radiotherapy sessions using real-time video. Method...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2025-05-01
|
| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/130186/download/pdf/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850184139441963008 |
|---|---|
| author | Yang Zhang Ziwen Wei Zhihua Liu Xiaolong Wu Junchao Qian |
| author_facet | Yang Zhang Ziwen Wei Zhihua Liu Xiaolong Wu Junchao Qian |
| author_sort | Yang Zhang |
| collection | DOAJ |
| description | Purpose: Incorrect patient positioning during radiotherapy can significantly impact treatment efficacy and pose potential risks. This study aims to develop a model that can rapidly and effectively monitor the patient’s postures during radiotherapy sessions using real-time video. Methods: The neural network utilized in this research employed a two-stream architecture, consisting of spatial and temporal streams. For the spatial stream, RGB frames from the videos were directly used as input. In the temporal stream, representative frames were extracted from the video to construct stacked grayscale 3-channel images (SG3I) frames. This approach enabled capturing motion information through a large-scale dataset pre-trained 2D convolutional neural network (CNN), eliminating the need for computationally expensive optical flow calculations. Additionally, an improved lightweight network architecture was employed. The model was trained and tested using volunteer videos collected from a radiotherapy center in a hospital. Results: The results demonstrated that the proposed model outperforms existing methods in terms of detection accuracy while achieving higher efficiency in frame generation. Conclusion: In this study, we introduced a cost-effective and highly accurate method for recognizing patient’s postures during radiotherapy. This approach could be readily deployed in any radiotherapy facility, ensuring treatment precision and patient safety.  |
| format | Article |
| id | doaj-art-873f7ba70d334dadb3bd9a16a86c478e |
| institution | OA Journals |
| issn | 0948-6968 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj-art-873f7ba70d334dadb3bd9a16a86c478e2025-08-20T02:17:06ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682025-05-0131664866510.3897/jucs.130186130186Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel ImagesYang Zhang0Ziwen Wei1Zhihua Liu2Xiaolong Wu3Junchao Qian4Hefei Institutes of Physical ScienceHefei Institutes of Physical ScienceHefei Institutes of Physical ScienceHefei Institutes of Physical ScienceAnhui Jianzhu UniversityPurpose: Incorrect patient positioning during radiotherapy can significantly impact treatment efficacy and pose potential risks. This study aims to develop a model that can rapidly and effectively monitor the patient’s postures during radiotherapy sessions using real-time video. Methods: The neural network utilized in this research employed a two-stream architecture, consisting of spatial and temporal streams. For the spatial stream, RGB frames from the videos were directly used as input. In the temporal stream, representative frames were extracted from the video to construct stacked grayscale 3-channel images (SG3I) frames. This approach enabled capturing motion information through a large-scale dataset pre-trained 2D convolutional neural network (CNN), eliminating the need for computationally expensive optical flow calculations. Additionally, an improved lightweight network architecture was employed. The model was trained and tested using volunteer videos collected from a radiotherapy center in a hospital. Results: The results demonstrated that the proposed model outperforms existing methods in terms of detection accuracy while achieving higher efficiency in frame generation. Conclusion: In this study, we introduced a cost-effective and highly accurate method for recognizing patient’s postures during radiotherapy. This approach could be readily deployed in any radiotherapy facility, ensuring treatment precision and patient safety. https://lib.jucs.org/article/130186/download/pdf/Posture recognitionRadiotherapySpatial-tempora |
| spellingShingle | Yang Zhang Ziwen Wei Zhihua Liu Xiaolong Wu Junchao Qian Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images Journal of Universal Computer Science Posture recognition Radiotherapy Spatial-tempora |
| title | Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images |
| title_full | Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images |
| title_fullStr | Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images |
| title_full_unstemmed | Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images |
| title_short | Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images |
| title_sort | posture monitoring of patients in radiotherapy scenarios based on stacked grayscale 3 channel images |
| topic | Posture recognition Radiotherapy Spatial-tempora |
| url | https://lib.jucs.org/article/130186/download/pdf/ |
| work_keys_str_mv | AT yangzhang posturemonitoringofpatientsinradiotherapyscenariosbasedonstackedgrayscale3channelimages AT ziwenwei posturemonitoringofpatientsinradiotherapyscenariosbasedonstackedgrayscale3channelimages AT zhihualiu posturemonitoringofpatientsinradiotherapyscenariosbasedonstackedgrayscale3channelimages AT xiaolongwu posturemonitoringofpatientsinradiotherapyscenariosbasedonstackedgrayscale3channelimages AT junchaoqian posturemonitoringofpatientsinradiotherapyscenariosbasedonstackedgrayscale3channelimages |