  The segmentation of prostate whole gland and transition zone in Diffusion
Weighted MRI (DWI) are the first step in designing computer-aided detection
algorithms for prostate cancer. However, variations in MRI acquisition
parameters and scanner manufacturing result in different appearances of
prostate tissue in the images. Convolutional neural networks (CNNs) which have
shown to be successful in various medical image analysis tasks including
segmentation are typically sensitive to the variations in imaging parameters.
This sensitivity leads to poor segmentation performance of CNNs trained on a
source cohort and tested on a target cohort from a different scanner and hence,
it limits the applicability of CNNs for cross-cohort training and testing.
Contouring prostate whole gland and transition zone in DWI images are
time-consuming and expensive. Thus, it is important to enable CNNs pretrained
on images of source domain, to segment images of target domain with minimum
requirement for manual segmentation of images from the target domain. In this
work, we propose a transfer learning method based on a modified U-net
architecture and loss function, for segmentation of prostate whole gland and
transition zone in DWIs using a CNN pretrained on a source dataset and tested
on the target dataset. We explore the effect of the size of subset of target
dataset used for fine-tuning the pre-trained CNN on the overall segmentation
accuracy. Our results show that with a fine-tuning data as few as 30 patients
from the target domain, the proposed transfer learning-based algorithm can
reach dice score coefficient of 0.80 for both prostate whole gland and
transition zone segmentation. Using a fine-tuning data of 115 patients from the
target domain, dice score coefficient of 0.85 and 0.84 are achieved for
segmentation of whole gland and transition zone, respectively, in the target
domain.
