  Purpose: A conventional 2D UNet convolutional neural network (CNN)
architecture may result in ill-defined boundaries in segmentation output.
Several studies imposed stronger constraints on each level of UNet to improve
the performance of 2D UNet, such as SegNet. In this study, we investigated 2D
SegNet and a proposed conditional random field insert (CRFI) for zonal prostate
segmentation from clinical T2-weighted MRI data.
  Methods: We introduced a new methodology that combines SegNet and CRFI to
improve the accuracy and robustness of the segmentation. CRFI has feedback
connections that encourage the data consistency at multiple levels of the
feature pyramid. On the encoder side of the SegNet, the CRFI combines the input
feature maps and convolution block output based on their spatial local
similarity, like a trainable bilateral filter. For all networks, 725 2D images
(i.e., 29 MRI cases) were used in training; while, 174 2D images (i.e., 6
cases) were used in testing.
  Results: The SegNet with CRFI achieved the relatively high Dice coefficients
(0.76, 0.84, and 0.89) for the peripheral zone, central zone, and whole gland,
respectively. Compared with UNet, the SegNet+CRFIs segmentation has generally
higher Dice score and showed the robustness in determining the boundaries of
anatomical structures compared with the SegNet or UNet segmentation. The SegNet
with a CRFI at the end showed the CRFI can correct the segmentation errors from
SegNet output, generating smooth and consistent segmentation for the prostate.
  Conclusion: UNet based deep neural networks demonstrated in this study can
perform zonal prostate segmentation, achieving high Dice coefficients compared
with those in the literature. The proposed CRFI method can reduce the fuzzy
boundaries that affected the segmentation performance of baseline UNet and
SegNet models.
