Medical physics · 2025
Robust real-time segmentation of bio-morphological features in human cherenkov imaging during radiotherapy via deep learning.
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Wang Shiru, Chen Yao, Jarvis Lesley A, Tang Yucheng, Gladstone David J, Samkoe Kimberley S, Pogue Brian W, Bruza Petr, Zhang Rongxiao
Аннотация
BACKGROUND: Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during radiation therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However, no concerted analysis of this biological feature-based tracking has been utilized until now because of the slow speed and accuracy of conventional image processing for feature segmentation.
PURPOSE: This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing.
MATERIALS AND METHODS: To address the challenge of limited annotation of bio-morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction.
RESULTS: The well-trained model was tested on clinical Cherenkov dataset which was not used in fine-tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance.
CONCLUSIONS: The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.
PURPOSE: This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing.
MATERIALS AND METHODS: To address the challenge of limited annotation of bio-morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction.
RESULTS: The well-trained model was tested on clinical Cherenkov dataset which was not used in fine-tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance.
CONCLUSIONS: The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.