Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.

ABSTRACT:

Background

Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma.

Methods

This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in t

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11009801 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature