生物医学工程研究2024，Vol.43Issue(3)：181-189,9.DOI:10.19529/j.cnki.1672-6278.2024.03.02
多尺度特征融合的膀胱癌磁共振成像分割算法
Segmentation algorithm of bladder cancer MRI image based on multi-scale feature fusion
摘要
Abstract
In view of the small tumor area,blurred bladder wall boundary and pixel imbalance in bladder cancer magnetic reso-nance imaging(MRI)images,we proposed a multi-scale feature fusion segmentation algorithm for bladder cancer MRI images based on the feature fusion process and the correlation between different image pixels.In the coding stage,a multi-scale feature fusion module was designed to learn the feature information of different encoders,so as to extract richer information of bladder wall and tumor.The pixel contrast module was designed in the decoding stage to increase the difference between bladder wall and bladder tumor,solve the low contrast and pixel imbalance,improve the segmentation performance of the adjacent boundary region between bladder wall and tumor,and realize multi-region segmentation of bladder cancer.The experiments were conducted on the MRI dataset of bladder cancer,the results showed that the Dice of the algorithm in the bladder wall and tumor area was 89.70%and 89.13%,the intersection over u-nion(IoU)was 81.32%and 80.51%,the Hausdorff distance(HD)was 1.30 and 1.37,respectively.Compared with the existing algo-rithms,the segmentation effect was improved.This research can better assist clinical imaging diagnosis and provide important basis for subsequent tumor staging and clinical diagnosis and treatment.关键词
多尺度特征融合/膀胱癌/T2加权MRI/Swin Transformer/对比学习Key words
Multi-scale feature fusion/Bladder cancer/T2-weighted MRI/Swin Transformer/Contrast learning分类
医药卫生引用本文复制引用
姜梓垚,李翔,魏本征..多尺度特征融合的膀胱癌磁共振成像分割算法[J].生物医学工程研究,2024,43(3):181-189,9.基金项目
国家自然科学基金资助项目(62372280,61872225) （62372280,61872225）
山东省自然科学基金资助项目(ZR2020KF013,ZR2019ZD04) （ZR2020KF013,ZR2019ZD04）
青岛市科技惠民示范专项项目(23-2-8-smjk-2-nsh). （23-2-8-smjk-2-nsh）
