  Prostate cancer is the most common malignant tumors in men but prostate
Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole
prostate gland segmentation, the capability to differentiate between the blurry
boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to
differential diagnosis, since tumor's frequency and severity differ in these
regions. To tackle the prostate zonal segmentation task, we propose a novel
Convolutional Neural Network (CNN), called USE-Net, which incorporates
Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are
added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec
USE-Net). This study evaluates the generalization ability of CNN-based
architectures on three T2-weighted MRI datasets, each one consisting of a
different number of patients and heterogeneous image characteristics, collected
by different institutions. The following mixed scheme is used for
training/testing: (i) training on either each individual dataset or multiple
prostate MRI datasets and (ii) testing on all three datasets with all possible
training/testing combinations. USE-Net is compared against three
state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale
Dense Network), along with a semi-automatic continuous max-flow model. The
results show that training on the union of the datasets generally outperforms
training on each dataset separately, allowing for both intra-/cross-dataset
generalization. Enc USE-Net shows good overall generalization under any
training condition, while Enc-Dec USE-Net remarkably outperforms the other
methods when trained on all datasets. These findings reveal that the SE blocks'
adaptive feature recalibration provides excellent cross-dataset generalization
when testing is performed on samples of the datasets used during training.

Computer Vision and Pattern Recognition;Machine Learning