基于圖卷積的自適應(yīng)特征融合MRI腦腫瘤分割方法

打開文本圖片集

中圖分類號：TP391.41；R739.41 文獻標識碼：A DOI：10.7535/hbkd.2025yx04005

Graph convolution-based adaptive feature fusion method for MRI brain tumor segmentation

ZHANG Ye1，ZHANG Muqing2，YUAN Xuegang1，NIU Datian1 （1.School of Science，Dalian Minzu University，Dalian，Liaoning1166oo，China; 2.School of Computer Science and Enginering，Dalian Minzu University，Dalian，Liaoning l166oo，China）

Abstract：Toaddress theisues ofinsuffcient global information capture and inadequatedeep semantic information fusion in the U-Netmodel for MRIbrain tumor segmentation，anovel braintumor segmentation network，ASGU-Net was proposed.

The algorithm wasbasedon 3D U-Net，incorporating agraph convolution inferencemodule tocaptureadditional long-range contextualfeatures.Aditionally，dynamicsnakeconvolution（DSConv）was introduced inthe encoder-decodertobetter accommodate the varied shapes of tumors，enhancing edge feature extractionand improving segmentation acuracy. Furthermore，anadaptivespatial featurefusion（ASFF）modulewas introduced in the decoder toenhance the feature fusion efect byintegrating semantic informationcaptured by multipleencoderblocks.Theevaluationonthepubliclyavailable BraT 2019—2021 datasets shows that the Dice values for whole tumor，tumor core and enhanced tumor are 90.70%/90.70% 1 91.00% ， 84.90%/84.00%/88.80% and 77.30%/77.40%/82.50% ，respectively. The experimental results demonstrate the effectiveessofASGU-NetinthebraintumorsegmentationtaskASGU-Netcaneectiveladdressstheissuesofiadequateglobal informationcaptureand feature fusion，providing effective reference for high-precisionautomatedbrain tumorsegmentation.

KeyWords：computer neuralnetwork；brain tumor segmentation；3D U-Net；graph convolution inferencebotteneck layer; dynamic snake convolution;adaptive spatial feature fusion

腦腫瘤是腦部異常細胞形成的腫塊，嚴重威脅人類的健康與生命。（剩余15198字）