Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDeep learning network for segmentation of the prostate gland with median lobe enlargement in T2-weighted MR images: comparison with manual segmentation method
Anno di pubblicazione2021
FormatoElettronico
Autore/iSalvaggio G.; Comelli A.; Portoghese M.; Cutaia G.; Cannella R.; Vernuccio F.; Stefano A.; Dispensa N.; La Tona G.; Salvaggio L.; Calamia M.; Gagliardo C.; Lagalla R.; Midiri M.
Affiliazioni autoriSection of Radiology - BiND, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy. Ri.Med Foundation, Palermo, Italy. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy. Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, Palermo, Italy. Discipline Chirurgiche, Oncologiche e Stomatologiche - Unità operativa di Urologia, Università degli Studi di Palermo, Palermo, Italy
Autori CNR e affiliazioni
  • ALBERT COMELLI
  • ALESSANDRO STEFANO
Lingua/e
  • inglese
AbstractPurpose Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. Materials and Methods One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate volumes obtained with ellipsoid formula, manual segmentation, and automated segmentation. To provide an evaluation of the similarity or difference to manual segmentation, sensitivity, positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric difference (VD) were calculated. Results Differences between prostate volume obtained from ellipsoid formula vs manual segmentation and vs automatic segmentation were statistically significant (p<0.049318 and p<0.034305, respectively), while no statistical difference was found between volume obtained from manual vs automatic segmentation (p= 0.438045). The performance of ENet versus manual segmentations was good providing a sensitivity of 93.51%, a PPV of 87.93%, a DSC of 90.38%, a VOE of 17.32% and a VD of 6.85%. Conclusion The presence of median lobe enlargement may lead to MRI volume overestimation when using the ellipsoid formula so that a segmentation method is recommended. ENet volume estimation showed great accuracy in evaluation of prostate volume similar to that of manual segmentation.
Lingua abstractinglese
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RivistaCurrent problems in diagnostic radiology
Attiva dal 1976
Editore: Elsevier. - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0363-0188
Titolo chiave: Current problems in diagnostic radiology
Titolo proprio: Current problems in diagnostic radiology.
Titolo abbreviato: Curr. probl. diagn. radiol.
Titoli alternativi:
  • Diagnostic radiology
  • CPDR
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DOI10.1067/j.cpradiol.2021.06.006
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveDeep learningSegmentationProstateMRIENet
Link (URL, URI)https://www.sciencedirect.com/science/article/abs/pii/S0363018821001067?via%3Dihub
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Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
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