Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis: Neural Network-based Methods for Liver Semantic Segmentation.
The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 22 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
10/14/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180518736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180518736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 10/14/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180518736 180518736 180518736 10.1007/s10916-024-02115-6 180518736 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis: Neural Network-based Methods for Liver Semantic Segmentation. aug: au: Delmoral, Jessica C. R.S. Tavares, João Manuel affil: https://ror.org/043pwc612 Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal sug: subj: Artificial Intelligence Utilization Liver Radiography Tomography, X-Ray Computed Neural Networks (Computer) Methods Bibliometrics Semantics Algorithms Human Systematic Review Image Processing, Computer Assisted Liver Neoplasms Radiography Deep Learning Methods Machine Learning Methods Benchmarking Liver Anatomy and Histology Carcinoma, Hepatocellular Radiography Contrast Media Quality of Health Care Magnetic Resonance Imaging Imaging, Three-Dimensional Descriptive Statistics ab: The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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