Journal of medical imaging (Bellingham, Wash.) · 2026
Deep-learning-based multiple organ segmentation for CT scout images: applications to automatic CT planning.
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Fang Kaylee W, Wang Sen, Medrano Maria Jose, Sun Liyan, Stevens Grant M, Tse Justin R, Wang Adam S
Аннотация
PURPOSE: Computed tomography (CT) scout images are used in CT planning to set the anatomic scan range and optimize the radiation dose. Manual exam planning is highly variable and contributes to excess radiation dose delivered to patients. We present a multi-organ segmentation system for CT scout images with a direct application for automatic scan range prediction, an important component of CT planning.
APPROACH: Variants of a U-Net model with a ResNet-50 encoder were trained to segment 35 distinct anatomic structures from frontal and lateral scout images and evaluated on their performance. Organ boundaries generated by the trained models were used to automatically set the scan range for chest-abdomen-pelvis and abdomen-pelvis CT protocols from frontal and lateral scout images.
RESULTS: The best-performing models produced organ segmentations with mean Dice scores of 0.823 ± 0.014 for the frontal scout and 0.784 ± 0.013 for the lateral scout. Predicted scan ranges covered ground-truth anatomy for 87% to 100% of test cases, reduced variability in six out of eight tested scan limits, and produced shorter median scan lengths for three out of four scout-to-protocol prediction tasks.
CONCLUSIONS: The presented models effectively segment a wide variety of organs from frontal and lateral scout images and demonstrate promising clinical applications to improved scan range prediction and automatic CT planning.
APPROACH: Variants of a U-Net model with a ResNet-50 encoder were trained to segment 35 distinct anatomic structures from frontal and lateral scout images and evaluated on their performance. Organ boundaries generated by the trained models were used to automatically set the scan range for chest-abdomen-pelvis and abdomen-pelvis CT protocols from frontal and lateral scout images.
RESULTS: The best-performing models produced organ segmentations with mean Dice scores of 0.823 ± 0.014 for the frontal scout and 0.784 ± 0.013 for the lateral scout. Predicted scan ranges covered ground-truth anatomy for 87% to 100% of test cases, reduced variability in six out of eight tested scan limits, and produced shorter median scan lengths for three out of four scout-to-protocol prediction tasks.
CONCLUSIONS: The presented models effectively segment a wide variety of organs from frontal and lateral scout images and demonstrate promising clinical applications to improved scan range prediction and automatic CT planning.