Automatic Scan Range Delimitation in Chest CT Using Deep Learning

Abstract

Purpose To develop and evaluate fully automatic scan range delimitation for chest CT by using deep learning. Materials and Methods For this retrospective study, scan ranges were annotated by two expert radiologists in consensus in 1149 (mean age, 65 years ± 16 [standard deviation]; 595 male patients) chest CT topograms acquired between March 2002 and February 2019 (350 with pleural effusion, 376 with atelectasis, 409 with neither, 14 with both). A conditional generative adversarial neural network was trained on 1000 randomly selected topograms to generate virtual scan range delimitations. On the remaining 149 topograms the software-based scan delimitations, scan lengths, and estimated radiation exposure were compared with those from clinical routine. For statistical analysis an equivalence test (two one-sided t tests) was used, with equivalence limits of 10 mm. Results The software-based scan ranges were similar to the radiologists’ annotations, with a mean Dice score coefficient of 0.99 ± 0.01 and an absolute difference of 1.8 mm ± 1.9 and 3.3 mm ± 5.6 at the upper and lower boundary, respectively. An equivalence test indicated that both scan range delimitations were similar (P < .001). The software-based scan delimitation led to shorter scan ranges compared with those used in clinical routine (298.2 mm ± 32.7 vs 327.0 mm ± 42.0; P < .001), resulting in a lower simulated total radiation exposure (3.9 mSv ± 3.0 vs 4.2 mSv ± 3.3; P < .001). Conclusion A conditional generative adversarial neural network was capable of automating scan range delimitation with high accuracy, potentially leading to shorter scan times and reduced radiation exposure. Keywords: Adults and Pediatrics, CT, Computer Applications-Detection/Diagnosis, Convolutional Neural Network (CNN), Lung, Radiation Safety, Segmentation, Supervised learning, Thorax © RSNA, 2021 Supplemental material is available for this article.

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