Improved UNet‐based magnetic resonance imaging segmentation of demyelinating diseases with small lesion regions

Abstract

Accurate magnetic resonance imaging (MRI) segmentation plays a critical role in the diagnosis and treatment of demyelinating diseases. But the existing automatic segmentation methods are not suitable for the segmentation of demyelinating lesions with small lesion size, highly diffuse edges and complex boundary shapes. An improved model is proposed for demyelinating diseases MRI segmentation based on the U‐shaped structure convolution neural networks (UNet). A context information weighting fusion (CIWF) module and a modified channel attention (MCA) module are developed and embedded in UNet to address the small lesion region and diffuse edge issues. The CIWF module can dynamically screen and fuse shallow and deep features at different stages, making the model pay more attention to small lesions. The MCA module enables the model to learn diverse features by adding weights to the channel, which helps in diffuse edge segmentation. Comparisons with many existing methods on real‐world demyelinating disease MRI segmentation dataset show that our method achieve the highest Dice metric.