  Three-dimensional medical image segmentation is one of the most important
problems in medical image analysis and plays a key role in downstream diagnosis
and treatment. Recent years, deep neural networks have made groundbreaking
success in medical image segmentation problem. However, due to the high
variance in instrumental parameters, experimental protocols, and subject
appearances, the generalization of deep learning models is often hindered by
the inconsistency in medical images generated by different machines and
hospitals. In this work, we present StyleSegor, an efficient and easy-to-use
strategy to alleviate this inconsistency issue. Specifically, neural style
transfer algorithm is applied to unlabeled data in order to minimize the
differences in image properties including brightness, contrast, texture, etc.
between the labeled and unlabeled data. We also apply probabilistic adjustment
on the network output and integrate multiple predictions through ensemble
learning. On a publicly available whole heart segmentation benchmarking dataset
from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice
accuracy surpassing current state-of-the-art method and notably, an improvement
of the total score by 29.91\%. StyleSegor is thus corroborated to be an
accurate tool for 3D whole heart segmentation especially on highly inconsistent
data, and is available at https://github.com/horsepurve/StyleSegor.
