Background Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure. Purpose To develop a deep learning model to segment anatomical structures in scout views to optimize scan ranges and reduce radiation. Materials and methods In this single-center retrospective study, 1146 patients undergoing CT between 2022 and 2025 were included. The model was trained on segmentations of 26 target structures in five regions (head, neck, chest, chest-to-pelvis, abdomen-to-pelvis), transferred to scout views and manually corrected. Performance was evaluated using the Dice-Sørensen coefficient and normalized surface distance on an internal test set of 100 patients and 36 external chest CTs. Automated versus manual scan planning was compared in 61 internal (chest, upper abdomen, head) and 14 external (chest) CTs, with z-axis coverage and dose-length product. Results 1146 patients (mean age, 63 ± 17 years; 577 men) were included. For target structures in five regions, mean DSC and NSD were 0.93 and 0.88. External mean DSC across chest targets was 0.851 ± 0.051. Automated planning captured relevant anatomy in 98% of internal and 92.9% of external CTs. Scan length significantly decreased for automated planning in the internal test cohort (chest 50 mm (15%), p < 0.001; upper abdomen 60 mm (25%), p < 0.001; cranial 19 mm (11%), p < 0.001), yielding corresponding DLP reductions of 19%, 25% and 11%, respectively. In the external cohort, scan length decreased by 115 mm (28.7%, p < 0.0001) with a corresponding 28.5% DLP reduction. Conclusion The proposed model enables reliable automated CT scan planning and reduces overscanning and radiation exposure without compromising diagnostic quality. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Can segmentation of anatomical structures on CT scout views enable automated scan planning to reduce overscanning and unnecessary radiation exposure? <Emphasis Type="BoldItalic">Findings</Emphasis> A deep learning model segmented planning-relevant anatomy on CT scout views and reduced scan length and dose while preserving anatomical coverage. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Unnecessary radiation from overscanning is a patient safety concern. Automated CT planning with the proposed deep learning model reduces radiation exposure while ensuring full anatomical coverage for the evaluated scan regions. Graphical Abstract

TOPOS: target organ prediction on scout views for automated CT scan planning

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2026年5月14日

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Sebastian Ziegelmayer,
Tristan Lemke,
Markus Graf,
Su Hwan Kim,
Lukas Lemke,
Christian J. Mertens,
Felix Busch,
Dominik Weller,
Marcus R. Makowski,
Lisa C. Adams,
Keno K. Bressem

摘要

Background

Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure.

Purpose

To develop a deep learning model to segment anatomical structures in scout views to optimize scan ranges and reduce radiation.

Materials and methods

In this single-center retrospective study, 1146 patients undergoing CT between 2022 and 2025 were included. The model was trained on segmentations of 26 target structures in five regions (head, neck, chest, chest-to-pelvis, abdomen-to-pelvis), transferred to scout views and manually corrected. Performance was evaluated using the Dice-Sørensen coefficient and normalized surface distance on an internal test set of 100 patients and 36 external chest CTs. Automated versus manual scan planning was compared in 61 internal (chest, upper abdomen, head) and 14 external (chest) CTs, with z-axis coverage and dose-length product.

Results

1146 patients (mean age, 63 ± 17 years; 577 men) were included. For target structures in five regions, mean DSC and NSD were 0.93 and 0.88. External mean DSC across chest targets was 0.851 ± 0.051. Automated planning captured relevant anatomy in 98% of internal and 92.9% of external CTs. Scan length significantly decreased for automated planning in the internal test cohort (chest 50 mm (15%), p < 0.001; upper abdomen 60 mm (25%), p < 0.001; cranial 19 mm (11%), p < 0.001), yielding corresponding DLP reductions of 19%, 25% and 11%, respectively. In the external cohort, scan length decreased by 115 mm (28.7%, p < 0.0001) with a corresponding 28.5% DLP reduction.

Conclusion

The proposed model enables reliable automated CT scan planning and reduces overscanning and radiation exposure without compromising diagnostic quality.

Key Points

Question Can segmentation of anatomical structures on CT scout views enable automated scan planning to reduce overscanning and unnecessary radiation exposure?

Findings A deep learning model segmented planning-relevant anatomy on CT scout views and reduced scan length and dose while preserving anatomical coverage.

Clinical relevance Unnecessary radiation from overscanning is a patient safety concern. Automated CT planning with the proposed deep learning model reduces radiation exposure while ensuring full anatomical coverage for the evaluated scan regions.

Graphical Abstract