Aisberg: dal 2009 è l'archivio ad accesso aperto dell'Università di Bergamo; dal 2011 è anche l'anagrafe della ricerca di Ateneo.
L'archivio è un'implementazione di IRIS, il sistema informativo per la ricerca sviluppato da Cineca.
EnglishProstate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.
(2020). CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study . Retrieved from http://hdl.handle.net/10446/178200
| Titolo: | CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study | |
| Tutti gli autori: | Rundo, Leonardo; Han, Changhee; Zhang, Jin; Hataya, Ryuichiro; Nagano, Yudai; Militello, Carmelo; Ferretti, Claudio; Nobile, Marco S.; Tangherloni, Andrea; Gilardi, Maria Carla; Vitabile, Salvatore; Nakayama, Hideki; Mauri, Giancarlo | |
| Data di pubblicazione: | 2020 | |
| Abstract (eng): | Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets. | |
| Nelle collezioni: | 1.2.01 Contributi in volume (Capitoli o Saggi) - Book Chapters/Essays |
File allegato/i alla scheda:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2020_Book_NeuralApproachesToDynamicsOfSi_Tangherloni.pdf | publisher's version - versione editoriale | Licenza default Aisberg | Testo non consultabile | |
| 1903.12571.pdf | postprint - versione referata/accettata senza referaggio | Licenza default Aisberg | Open AccessVisualizza/Apri |
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