Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithometry: Combined Deep Learning and Thresholding Methods.
Purpose: To develop and validate a deep learning and thresholding-based model for automatic kidney stone detection and scoring according to S.T.O.N.E. nephrolithometry.Procedures: Abdominal noncontrast computed tomography (NCCT) images were retrospectively archived from February 2018 to April 2019 f...
| Publicado en: | Molecular Imaging & Biology Vol. 23; no. 3; pp. 436 - 446 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
|
| 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=150151446&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150151446 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15361632 KJU jtl: Molecular Imaging & Biology issn: 15361632 maglogo: N pubinfo: dt: Jun2021 vid: 23 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150151446 146647065 150151446 NLM33108801 10.1007/s11307-020-01554-0 NLM33108801 150151446 ppf: 436 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithometry: Combined Deep Learning and Thresholding Methods. aug: au: Cui, Yingpu Sun, Zhaonan Ma, Shuai Liu, Weipeng Wang, Xiangpeng Zhang, Xiaodong Wang, Xiaoying affil: Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, 100034, Beijing, China sug: subj: Information Science Kidney Calculi Tomography, X-Ray Computed Methods Predictive Value of Tests Severity of Illness Indices Aged Retrospective Design Contrast Media Female Middle Age Algorithms Imaging, Three-Dimensional Pharmacokinetics Male Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Purpose: To develop and validate a deep learning and thresholding-based model for automatic kidney stone detection and scoring according to S.T.O.N.E. nephrolithometry.Procedures: Abdominal noncontrast computed tomography (NCCT) images were retrospectively archived from February 2018 to April 2019 for three parts: a segmentation dataset (n = 167), a hydronephrosis classification dataset (n = 282), and test dataset (n = 117). The model consisted of four steps. First, the 3D U-Nets for kidney and renal sinus segmentation were developed. Second, the deep 3D dual-path networks for hydronephrosis grading were developed. Third, the thresholding methods were used to detect and segment stones in the renal sinus region. The stone size, CT attenuation, and tract length were calculated from the segmented stone region. Fourth, the stone's location was determined. The stone detection performance was estimated with sensitivity and positive predictive value (PPV). The hydronephrosis grading and stone size, tract length, number of involved calyces, and essence grading were estimated with the area under the curve (AUC) method and linear-weighted κ statistics, respectively.Results: The stone detection algorithm reached a sensitivity of 95.9 % (236/246) and a PPV of 98.7 % (236/239). The hydronephrosis classification algorithm achieved an AUC of 0.97. The scoring model results showed good agreement with radiologist results for the stone size, tract length, number of involved calyces, and essence grading (κ = 0.95, 95 % confidence interval [CI]: 0.92, 0.98; κ = 0.97, 95 % CI: 0.95, 1.00; κ = 0.95, 95 % CI: 0.92, 0.98; and κ = 0.97, 95 % CI: 0.94, 1.00), respectively.Conclusions: The scoring model was constructed that can automatically detect and score stones in NCCT images. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|