  Real-time localization of prostate gland in trans-rectal ultrasound images is
a key technology that is required to automate the ultrasound guided prostate
biopsy procedures. In this paper, we propose a new deep learning based approach
which is aimed at localizing several prostate landmarks efficiently and
robustly. We propose a multitask learning approach primarily to make the
overall algorithm more contextually aware. In this approach, we not only
consider the explicit learning of landmark locations, but also build-in a
mechanism to learn the contour of the prostate. This multitask learning is
further coupled with an adversarial arm to promote the generation of feasible
structures. We have trained this network using ~4000 labeled trans-rectal
ultrasound images and tested on an independent set of images with ground truth
landmark locations. We have achieved an overall Dice score of 92.6% for the
adversarially trained multitask approach, which is significantly better than
the Dice score of 88.3% obtained by only learning of landmark locations. The
overall mean distance error using the adversarial multitask approach has also
improved by 20% while reducing the standard deviation of the error compared to
learning landmark locations only. In terms of computational complexity both
approaches can process the images in real-time using standard computer with a
standard CUDA enabled GPU.
