Cast suppression in radiographs by generative adversarial networks (CROSBI ID 299197)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Hržić, Franko ; Žužić, Ivana ; Tschauner, Sebastian ; Štajduhar, Ivan
engleski
Cast suppression in radiographs by generative adversarial networks
Injured extremities commonly need to be immobilized by casts to allow proper healing. We propose a method to suppress cast superimpositions in pediatric wrist radiographs based on the cycle generative adversarial network (CycleGAN) model. We retrospectively reviewed unpaired pediatric wrist radiographs (n = 9672) and sampled them into 2 equal groups, with and without cast. The test subset consisted of 718 radiographs with cast. We evaluated different quadratic input sizes (256, 512, and 1024 pixels) for U-Net and ResNet-based CycleGAN architectures in cast suppression, quantitatively and qualitatively. The mean age was 11 ± 3 years in images containing cast (n = 4836), and 11 ± 4 years in castless samples (n = 4836). A total of 5956 X-rays had been done in males and 3716 in females. A U-Net 512 CycleGAN performed best (P ≤ .001). CycleGAN models successfully suppressed casts in pediatric wrist radiographs, allowing the development of a related software tool for radiology image viewers.
artificial intelligence ; diagnostic imaging ; radiography ; wrist ; child
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Podaci o izdanju
28 (12)
2021.
2687-2694
objavljeno
1067-5027
1527-974X
10.1093/jamia/ocab192
Povezanost rada
Kliničke medicinske znanosti, Računarstvo