Aydin Demircioğlu 1 , Moon-Sung Kim 1 , Magdalena Charis Stein 1 , Nika Guberina 1 , Lale Umutlu 1 , Kai Nassenstein 1 Show Affiliations »
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, 2021Supplemental material is available for this article. 2021 by the Radiological Society of North America, Inc.
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, 2021Supplemental material is available for this article. 2021 by the Radiological Society of North America, Inc.
Entities: Chemical
Year: 2021
PMID： 34136818 PMCID： PMC8204132 DOI： 10.1148/ryai.2021200211
Source DB: PubMed Journal: Radiol Artif Intell ISSN： 2638-6100