URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging.
Ultrasound is a widespread imaging modality, with special application in medical fields such as nephrology. However, automated approaches for ultrasound renal interpretation still pose some challenges: (1) the need for manual supervision by experts at various stages of the system, which prevents its...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1458 - 1475 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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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=179554142&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554142 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554142 179554142 179554142 10.1007/s10278-024-01055-4 179554142 ppf: 1458 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging. aug: au: Molina-Moreno, Miguel González-Díaz, Iván Rivera Gorrín, Maite Burguera Vion, Víctor Díaz-de-María, Fernando affil: https://ror.org/03ths8210 Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911, Leganés, Spain sug: subj: Kidney Diseases Ultrasonography Diagnostic Imaging Methods Diagnosis, Computer Assisted Automation Human Neural Networks (Computer) Kidney Diseases Pathology Kidney Anatomy and Histology Diagnostic Imaging Standards Descriptive Statistics Machine Learning Funding Source ab: Ultrasound is a widespread imaging modality, with special application in medical fields such as nephrology. However, automated approaches for ultrasound renal interpretation still pose some challenges: (1) the need for manual supervision by experts at various stages of the system, which prevents its adoption in primary healthcare, and (2) their limited considered taxonomy (e.g., reduced number of pathologies), which makes them unsuitable for training practitioners and providing support to experts. This paper proposes a fully automated computer-aided diagnosis system for ultrasound renal imaging addressing both of these challenges. Our system is based in a multi-task architecture, which is implemented by a three-branched convolutional neural network and is capable of segmenting the kidney and detecting global and local pathologies with no need of human interaction during diagnosis. The integration of different image perspectives at distinct granularities enhanced the proposed diagnosis. We employ a large (1985 images) and demanding ultrasound renal imaging database, publicly released with the system and annotated on the basis of an exhaustive taxonomy of two global and nine local pathologies (including cysts, lithiasis, hydronephrosis, angiomyolipoma), establishing a benchmark for ultrasound renal interpretation. Experiments show that our proposed method outperforms several state-of-the-art methods in both segmentation and diagnosis tasks and leverages the combination of global and local image information to improve the diagnosis. Our results, with a 87.41% of AUC in healthy-pathological diagnosis and 81.90% in multi-pathological diagnosis, support the use of our system as a helpful tool in the healthcare system. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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