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Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols.
van Velzen, Sanne G M; Lessmann, Nikolas; Velthuis, Birgitta K; Bank, Ingrid E M; van den Bongard, Desiree H J G; Leiner, Tim; de Jong, Pim A; Veldhuis, Wouter B; Correa, Adolfo; Terry, James G; Carr, John Jeffrey; Viergever, Max A; Verkooijen, Helena M; Isgum, Ivana.
Afiliação
  • van Velzen SGM; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Lessmann N; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Velthuis BK; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Bank IEM; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • van den Bongard DHJG; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Leiner T; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • de Jong PA; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Veldhuis WB; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Correa A; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Terry JG; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Carr JJ; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Viergever MA; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Verkooijen HM; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
  • Isgum I; From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht,
Radiology ; 295(1): 66-79, 2020 04.
Article em En | MEDLINE | ID: mdl-32043947
ABSTRACT
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories 0, 1-10, 11-100, 101-400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI] 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies / Risk_factors_studies Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies / Risk_factors_studies Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article