Head radiotherapy positioning guidance system based on feature recognition and automatic annotation: Clinical...

Positioning accuracy in radiotherapy is critical for treatment outcomes, especially in head tumor radiotherapy, where the target area is small and surrounded by dense organs, requiring higher precision.

To explore the feasibility of constructing a radiotherapy positioning guidance system using an RGB-D camera and deep learning algorithms, and analyze the positioning errors of the system in head radiotherapy localization.

This study proposes an innovative positioning method that integrates deep learning algorithms into the radiotherapy workflow. An RGB-D camera was used in both the CT simulation and radiotherapy rooms to capture patient surface and facial images, which were used to develop the DeepLab-Opt and Fast Face Marker Detector (FFMD) algorithms. DeepLab-Opt was applied for coarse positioning through surface contour extraction, whereas FFMD was used for fine positioning by detecting facial landmarks. In the CT simulation room, color and depth images were acquired to generate reference contour and 3D facial landmark data, which were stored in the patient positioning database. During treatment setup in the radiotherapy room, real-time calibrated images were acquired and compared with the archived reference data to provide deviation and calibration-rate feedback for therapist adjustment. The system performance was then compared with that of the traditional cross-laser positioning method. Using an alternating design, 22 patients with head tumors underwent positioning with the proposed system and the conventional method on different treatment days, and positioning accuracy was evaluated by MVCT verification.

The Mann-Whitney U test was used to compare the MVCT verification positioning deviation data from 246 cases. The system's positioning errors in the lateral, longitudinal, vertical directions, and roll were 1.73 ± 1.35 mm, 1.53 ± 1.20 mm, 0.82 ± 0.94 mm, and 0.69° ± 0.51°, respectively, all significantly lower than those of the traditional cross-laser positioning method (p < 0.05). Additionally, the method reduced the positioning and registration time from 345.9 ± 93.4 to 307.8 ± 36.2 s (p < 0.001), with MVCT verification passing on the first attempt, reducing the need for multiple verifications and effectively reducing the radiation dose the patient receives during positioning.

The radiotherapy positioning guidance system is feasible and can provide real-time feedback on the patient's outer contour and facial feature point deviations, achieving precise mapping between CT simulation positioning and treatment positioning. It effectively improves the accuracy and efficiency of head radiotherapy positioning, demonstrating strong clinical application potential.