CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

  • Leonardo RundoEmail author
  • Changhee Han
  • Jin Zhang
  • Ryuichiro Hataya
  • Yudai Nagano
  • Carmelo Militello
  • Claudio Ferretti
  • Marco S. Nobile
  • Andrea Tangherloni
  • Maria Carla Gilardi
  • Salvatore Vitabile
  • Hideki Nakayama
  • Giancarlo Mauri
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 151)


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.


Prostate zonal segmentation Prostate cancer Anatomical MRI Deep convolutional neural networks Cross-dataset generalization 



This work was partially supported by the Graduate Program for Social ICT Global Creative Leaders of The University of Tokyo by JSPS. We thank the Cannizzaro Hospital, Catania, Italy, for providing one of the imaging datasets analyzed in this study.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Leonardo Rundo
    • 1
    • 2
    Email author
  • Changhee Han
    • 3
  • Jin Zhang
    • 3
  • Ryuichiro Hataya
    • 3
  • Yudai Nagano
    • 3
  • Carmelo Militello
    • 2
  • Claudio Ferretti
    • 1
  • Marco S. Nobile
    • 1
  • Andrea Tangherloni
    • 1
  • Maria Carla Gilardi
    • 2
  • Salvatore Vitabile
    • 4
  • Hideki Nakayama
    • 3
  • Giancarlo Mauri
    • 1
  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly
  2. 2.Institute of Molecular Bioimaging and Physiology (IBFM), Italian National Research Council (CNR)Cefalù (PA)Italy
  3. 3.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  4. 4.Department of Biopathology and Medical BiotechnologiesUniversity of PalermoPalermoItaly

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