TY - JOUR
T1 - Gaussian Mixture Model Loss Functional for Brain MRI Segmentation With Deep Learning
AU - Ma , Lianhong
JO - Journal of Information and Computing Science
VL - 2
SP - 138
EP - 153
PY - 2024
DA - 2024/01
SN - 17
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22355.html
KW - Semi-supervised learning, Unsupervised Deep learning, Modified U-Net, Gaussian mixture model.
AB - <p style="text-align: justify;">Because of the excellent performance and fast speed of deep neural network, U-Net has become
the most popular network framework for medical image segmentation. For various specific image segmentation
tasks, researchers have proposed a series of U-Net related methods. However, on the one hand, due to the
inherent limitations of convolutional neural networks, the variants of U-Net still cannot model long-range
information well while maintaining detailed texture information. On the other hand, since medical images are
difficult to obtain a large number of high-quality semantic pixel-level annotations, it is difficult to use
supervised deep learning networks. To address these issues above, we proposed a modified U-Net structure
and a Gaussian mixture model (GMM) based loss function. This modified U-Net can be well applied to brain
MR image segmentation, which can not only restore the detailed information well, but also take into account
the relatively large-scale local information. The proposed GMM loss can be used for unsupervised training of
neural networks. It effectively alleviates the shortcomings of difficult access to medical image annotation data
and improves the performance of deep neural networks. In the experiments in this paper, the GMM loss
function can also be used as a regular term to assist supervised learning to achieve better results. Experimental
results on brain MR images demonstrate the superior performance of the proposed model.</p>