  Monitoring the progression of lesions is important for clinical response.
Summary statistics such as lesion volume are objective and easy to interpret,
which can help clinicians assess lesion growth or decay. CNNs are commonly used
in medical image segmentation for their ability to produce useful features
within large contexts and their associated efficient iterative patch-based
training. Many CNN architectures require hundreds of thousands parameters to
yield a good segmentation. In this work, an efficient, computationally
inexpensive CNN is implemented to estimate the number of lesion voxels in a
predefined patch size from magnetic resonance (MR) images. The output of the
CNN is interpreted as the conditional Poisson parameter over the patch,
allowing standard mini-batch gradient descent to be employed. The ISLES2015
(SISS) data is used to train and evaluate the model, which by estimating lesion
volume from raw features, accurately identified the lesion image with the
larger lesion volume for 86% of paired sample patches. An argument for the
development and use of estimating lesion volumes to also aid in model selection
for segmentation is made.
