  Deep learning-based methods achieved impressive results for the segmentation
of medical images. With the development of 3D fully convolutional networks
(FCNs), it has become feasible to produce improved results for multi-organ
segmentation of 3D computed tomography (CT) images. The results of multi-organ
segmentation using deep learning-based methods not only depend on the choice of
networks architecture, but also strongly rely on the choice of loss function.
In this paper, we present a discussion on the influence of Dice-based loss
functions for multi-class organ segmentation using a dataset of abdominal CT
volumes. We investigated three different types of weighting the Dice loss
functions based on class label frequencies (uniform, simple and square) and
evaluate their influence on segmentation accuracies. Furthermore, we compared
the influence of different initial learning rates. We achieved average Dice
scores of 81.3%, 59.5% and 31.7% for uniform, simple and square types of
weighting when the learning rate is 0.001, and 78.2%, 81.0% and 58.5% for each
weighting when the learning rate is 0.01. Our experiments indicated a strong
relationship between class balancing weights and initial learning rate in
training.
