  Prostate biopsy and image-guided treatment procedures are often performed
under the guidance of ultrasound fused with magnetic resonance images (MRI).
Accurate image fusion relies on accurate segmentation of the prostate on
ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g.,
speckle and shadowing) in ultrasound images limit the performance of automated
prostate segmentation techniques and generalizing these methods to new image
domains is inherently difficult. In this study, we address these challenges by
introducing a novel 2.5D deep neural network for prostate segmentation on
ultrasound images. Our approach addresses the limitations of transfer learning
and finetuning methods (i.e., drop in performance on the original training data
when the model weights are updated) by combining a supervised domain adaptation
technique and a knowledge distillation loss. The knowledge distillation loss
allows the preservation of previously learned knowledge and reduces the
performance drop after model finetuning on new datasets. Furthermore, our
approach relies on an attention module that considers model feature positioning
information to improve the segmentation accuracy. We trained our model on 764
subjects from one institution and finetuned our model using only ten subjects
from subsequent institutions. We analyzed the performance of our method on
three large datasets encompassing 2067 subjects from three different
institutions. Our method achieved an average Dice Similarity Coefficient (Dice)
of $94.0\pm0.03$ and Hausdorff Distance (HD95) of 2.28 $mm$ in an independent
set of subjects from the first institution. Moreover, our model generalized
well in the studies from the other two institutions (Dice: $91.0\pm0.03$; HD95:
3.7$mm$ and Dice: $82.0\pm0.03$; HD95: 7.1 $mm$).
