首页|多尺度特征融合的膀胱癌磁共振成像分割算法

多尺度特征融合的膀胱癌磁共振成像分割算法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对膀胱癌磁共振成像(magnetic resonance imaging,MRI)图像肿瘤区域面积小、膀胱壁边界模糊及像素不平衡等问题,本研究基于特征融合过程和不同图像像素之间的相关性,提出了一种多尺度特征融合的膀胱癌MRI图像分割算法.在编码阶段设计多尺度特征融合模块,用于学习不同编码器的多尺度信息,提取膀胱壁和肿瘤更加丰富的特征;在解码阶段设计的像素对比模块,可增加膀胱壁和膀胱肿瘤间的差异性,解决对比度低及像素不平衡问题,提高膀胱壁与肿瘤相邻边界区域的分割性能,实现膀胱癌多区域分割.本研究在膀胱癌MRI数据集上进行实验,结果显示,算法在膀胱壁和肿瘤区域的Dice分别为 89.70%和 89.13%、交并比(intersection over union,IoU)分别为 81.32%和 80.51%、豪斯多夫距离(Hausdorff distance,HD)分别为 1.30 和 1.37,分割结果较已有算法均有一定提升.本研究能较好地辅助临床影像学诊断,可为后续肿瘤分期和临床诊疗提供重要依据.
Segmentation algorithm of bladder cancer MRI image based on multi-scale feature fusion
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.

Multi-scale feature fusionBladder cancerT2-weighted MRISwin TransformerContrast learning

姜梓垚、李翔、魏本征

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山东中医药大学 医学人工智能研究中心,青岛 266112

山东中医药大学 青岛中医药科学院,青岛 266112

多尺度特征融合 膀胱癌 T2加权MRI Swin Transformer 对比学习

国家自然科学基金资助项目国家自然科学基金资助项目山东省自然科学基金资助项目山东省自然科学基金资助项目青岛市科技惠民示范专项项目

6237228061872225ZR2020KF013ZR2019ZD0423-2-8-smjk-2-nsh

2024

10.19529/j.cnki.1672-6278.2024.03.02

生物医学工程研究
山东生物医学工程学会 山东省医疗器械研究所 山东省千佛山医院

生物医学工程研究

CSTPCD
影响因子：0.512
ISSN：1672-6278
年,卷(期)：2024.43(3)
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