基于图卷积的自适应特征融合MRI脑肿瘤分割方法

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中图分类号：TP391.41；R739.41 文献标识码：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

脑肿瘤是脑部异常细胞形成的肿块，严重威胁人类的健康与生命。（剩余15198字）