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A Reinforcement Learning Framework for Medical Image Segmentation.pdf
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657k
资源说明：Abstract— This paper introduces a new method to medical
image segmentation using a reinforcement learning scheme.
We use this novel idea as an effective way to optimally find
the appropriate local thresholding and structuring element
values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and
its manually segmented version and takes some actions (i.e.,
different thresholding and structuring element values) to change
the environment (the quality of segmented image). The agent
is provided with a scalar reinforcement signal determined
objectively. The agent uses these objective reward/punishment
to explore/exploit the solution space. The values obtained using
this way can be used as valuable knowledge to fill a Q-matrix.
The reinforcement learning agent can use this knowledge for
similar ultrasound images as well. The results demonstrate high
potential for applying reinforcement learning in the field of
medical image segmentation
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