SE-ATT-YOLO- A deep learning driven ultrasound based respiratory motion compensation system for precision radiotherapy.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan; Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan.
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan.
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan.
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan; School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan.
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
- Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, 5-16-1 Omiya, Asahi-ku, Osaka 535-8585 Japan.
- PG & Research Department of Chemistry, Bishop Heber College, Tiruchirappalli-620017, India.
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan; School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan.
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan. Electronic address: [email protected].
Abstract
The therapeutic management of neoplasm employs high level energy beam to ablate malignant cells, which can cause collateral damage to adjacent normal tissue. Furthermore, respiration-induced organ motion, during radiotherapy can lead to significant displacement of neoplasms. In this work, a non-invasive ultrasound-based deep learning algorithm for respiratory motion compensation system (RMCS) was developed to mitigate the effect of respiratory motion induced neoplasm movement in radiotherapy. The deep learning algorithm generated based on modified YOLOv8n (You Only Look Once), by incorporating squeeze and excitation blocks for channel wise recalibration and enhanced attention mechanisms for spatial channel focus (SE-ATT-YOLO) to cope up with enhanced ultrasound image detection in real time scenario. The trained model was inferred with ultrasound movement of human diaphragm and tracked the bounding box coordinates using BoT-Sort, which drives the RMCS. The SE-ATT-YOLO model achieved mean average precision (mAP) of 0.88 which outperforms YOLOv8n with the value of 0.85. The root mean square error (RMSE) obtained from prerecorded respiratory signals with the compensated RMCS signal was calculated. The model achieved an inference speed of approximately 50 FPS. The RMSE values recorded were 4.342 for baseline shift, 3.105 for sinusoidal signal, 1.778 for deep breath, and 1.667 for slow signal. The SE-ATT-YOLO model outperformed all the results of previous models. The loss function uncertainty in YOLOv8n model was rectified in SE-ATT YOLO depicting the stability of the model. The model' stability, speed and accuracy of the model optimized the performance of the RMCS.