Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDeep Learning Whole-Gland and Zonal Prostate Segmentation on a Public MRI Dataset
Anno di pubblicazione2021
FormatoElettronico
Autore/iRenato Cuocolo 1 2, Albert Comelli 3, Alessandro Stefano 4, Viviana Benfante 4, Navdeep Dahiya 5, Arnaldo Stanzione 6, Anna Castaldo 6, Davide Raffaele De Lucia 6, Anthony Yezzi 5, Massimo Imbriaco 6
Affiliazioni autori1. Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy. 2. Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy. 3. Ri.MED Foundation, Palermo, Italy. 4. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy. 5. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. 6. Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Autori CNR e affiliazioni
  • VIVIANA BENFANTE
  • ALESSANDRO STEFANO
Lingua/e
  • inglese
AbstractBackground: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen. Purpose: This study compared different deep learning methods for whole-gland and zonal prostate segmentation. Study type: Retrospective. Population: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. Field strength/sequence: A 3 T, TSE T2 -weighted. Assessment: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two. Statistical tests: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance. Results: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet. Data conclusion: Deep learning networks can accurately segment the prostate using T2 -weighted images. Evidence level: 4 TECHNICAL EFFICACY: Stage 2.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da452
Pagine a459
Pagine totali8
RivistaJournal of magnetic resonance imaging (Online)
Attiva dal 1991
Editore: Wiley-Liss - New York
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1522-2586
Titolo chiave: Journal of magnetic resonance imaging (Online)
Titolo proprio: Journal of magnetic resonance imaging (Online)
Titolo abbreviato: J. magn. reson. imaging (Online)
Titolo alternativo: JMRI (Online)
Numero volume della rivista54
Fascicolo della rivista2
DOI10.1002/jmri.27585
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • PubMed (Codice:33634932)
  • ISI Web of Science (WOS) (Codice:000621871600001)
  • Scopus (Codice:2-s2.0-85101703147)
Parole chiavedeep learning; machine learning; magnetic resonance imaging; prostate; prostatic neoplasms.
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione16/02/2021
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati