FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manu...

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Autori principali:	Liting Fan, Yi Lei, Feng Song, Xiangfei Sun, Zhuowei Zhang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	BMC 2025-07-01
Serie:	BMC Medical Imaging
Soggetti:	Non-alcoholic fatty liver disease(NAFLD) CT images Deep learning Instance segmetation Severity scoring Aided diagnosis
Accesso online:	https://doi.org/10.1186/s12880-025-01805-y
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