Pregled bibliografske jedinice broj: 1148546
Cast suppression in radiographs by generative adversarial networks
Cast suppression in radiographs by generative adversarial networks // Journal of the american medical informatics association, 28 (2021), 12; 2687-2694 doi:10.1093/jamia/ocab192 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1148546 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Cast suppression in radiographs by generative
adversarial networks
Autori
Hržić, Franko ; Žužić, Ivana ; Tschauner, Sebastian ; Štajduhar, Ivan
Izvornik
Journal of the american medical informatics association (1067-5027) 28
(2021), 12;
2687-2694
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
artificial intelligence ; diagnostic imaging ; radiography ; wrist ; child
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Kliničke medicinske znanosti
POVEZANOST RADA
Projekti:
HRZZ-IP-2020-02-3770 - Strojno učenje za prijenos znanja u medicinskoj radiologiji (RadiologyNET) (Štajduhar, Ivan, HRZZ - 2020-02) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-15 - Razvoj postupaka temeljenih na strojnom učenju za prepoznavanje bolesti i ozljeda iz medicinskih slika (Štajduhar, Ivan, NadSve ) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- Social Science Citation Index (SSCI)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
- MEDLINE