2019 JRNL PP Annegreet van Opbroek - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Many medical image segmentation methods
are based on the supervised classification of voxels. Such
methods generally perform well when provided with a
training set that is representative of the test images to the
segment. However, problems may arise when training and
test data follow different distributions, for example, due
to differences in scanners, scanning protocols, or patient
groups. Under such conditions, weighting training images
according to distribution similarity have been shown to
greatly improve performance. However, this assumes that
a part of the training data is representative of the test data;
it does not make unrepresentative data more similar. We,
therefore, investigate kernel learning as a way to reduce
differences between training and test data and explore
the added value of kernel learning for image weighting.
We also propose a new image weighting method that minimizes
maximum mean discrepancy (MMD) between training
and test data, which enables the joint optimization of
image weights and kernel. Experiments on brain tissue,
white matter lesion, and hippocampus segmentation show
that both kernel learning and image weighting, when used
separately, greatly improve performance on heterogeneous
data. Here, MMD weighting obtains similar performance to
previously proposed image weighting methods. Combining
image weighting and kernel learning, optimized either individually
or jointly, can give a small additional improvement
in performance.