Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate
  Segmentation in TRUS Images
Weixi Yi Vasilis Stavrinides Zachary M. C. Baum Qianye Yang Dean C. Barratt Matthew J. Clarkson Yipeng Hu Shaheer U. Saeed
We propose Boundary-RL, a novel weakly supervised segmentation method that
utilises only patch-level labels for training. We envision the segmentation as
a boundary detection problem, rather than a pixel-level classification as in
previous works. This outlook on segmentation may allow for boundary delineation
under challenging scenarios such as where noise artefacts may be present within
the region-of-interest (ROI) boundaries, where traditional pixel-level
classification-based weakly supervised methods may not be able to effectively
segment the ROI. Particularly of interest, ultrasound images, where intensity
values represent acoustic impedance differences between boundaries, may also
benefit from the boundary delineation approach. Our method uses reinforcement
learning to train a controller function to localise boundaries of ROIs using a
reward derived from a pre-trained boundary-presence classifier. The classifier
indicates when an object boundary is encountered within a patch, as the
controller modifies the patch location in a sequential Markov decision process.
The classifier itself is trained using only binary patch-level labels of object
presence, which are the only labels used during training of the entire boundary
delineation framework, and serves as a weak signal to inform the boundary
delineation. The use of a controller function ensures that a sliding window
over the entire image is not necessary. It also prevents possible
false-positive or -negative cases by minimising number of patches passed to the
boundary-presence classifier. We evaluate our proposed approach for a
clinically relevant task of prostate gland segmentation on trans-rectal
ultrasound images. We show improved performance compared to other tested weakly
supervised methods, using the same labels e.g., multiple instance learning.