摘要
医学图像分割在临床诊疗和病理分析中具有重要的应用价值。近年来,去噪扩散模型在图像分割建模方面取得了显著成功,其能够更好地捕获图像中的复杂结构和细节信息。然而,利用去噪扩散模型进行医学图像分割的方法大多忽略了分割目标的边界不确定和区域模糊因素,从而造成了最终分割结果的不稳定性和不准确性。为了解决这一问题,提出了一种基于模糊逻辑驱动的医学图像扩散融合网络分割模型(FDiff-Fusion)。该模型通过将去噪扩散模型集成到经典U-Net网络中,有效地从输入医学图像中提取丰富的语义信息。由于医学图像的分割目标边界不确定性和区域模糊化现象普遍存在,因此在U-Net网络的跳跃路径上设计了一种模糊学习模块。该模块为输入的编码特征设置多个模糊隶属度函数,以描述特征点之间的相似程度,并对模糊隶属度函数应用模糊规则处理,从而增强了模型对不确定边界和模糊区域的建模能力。此外,为了提高模型分割结果的准确性和鲁棒性,在测试阶段引入了基于迭代注意力特征融合的方法。该方法将局部上下文信息添加到注意力模块中的全局上下文信息中,以融合每个去噪时间步的预测结果。实验结果显示,与现有的先进分割网络相比,FDiff-Fusion在BRATS 2020脑肿瘤数据集上获得的平均Dice分数和HD95距离分别为84.16%和2.473mm,在BTCV腹部多器官数据集上获得的平均Dice分数和HD95距离分别为83.82%和7.98mm,表现出良好的分割性能。
Medical image segmentation has important application value in clinical diagnosis,treatment and pathological analysis.In recent years,denoising diffusion models have achieved remarkable success in image segmentation modeling,which can better capture complex structure and detail information in images.However,most of the methods using the denoising diffusion model for medical image segmentation ignore the boundary uncertainty and region ambiguity of the segmentation target,resulting in the instability and inaccuracy of the final segmentation results.In order to solve this problem,a medical image diffusion fusion network segmentation model driven based on fuzzy logic(FDiff-Fusion)is proposed.By integrating the denoising diffusion model into the classical U-Net network,this model can effectively extract rich semantic information from inputting medical images.Since the boundary uncertainty and region blurring of medical image segmentation are common,a fuzzy learning module is designed on the jump path of U-Net network.The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points,and applies fuzzy rules to the fuzzy membership functions,thus enhancing the modeling ability of the model to the uncertain boundary and fuzzy region.In addition,in order to improve the accuracy and robustness of the model segmentation results,a method based on iterative attention feature fusion is introduced in the test phase,which adds local context information to the global context information in the attention module to fuse the prediction results of each denoising time step.Experimental results show that compared with existing advanced segmentation networks,the average Dice score and the average HD95 distance obtained by FDiff-Fusion on BRATS 2020 brain tumor dataset are 84.16%and 2.473mm,respectively.The mean Dice score and the mean HD95 distance obtained on BTCV abdominal multi-organ dataset are 83.41%and 7.98mm,respectively,showing good segmentation performance.
作者
耿胜
丁卫平
鞠恒荣
黄嘉爽
姜舒
王海鹏
GENG Sheng;DING Weiping;JU Hengrong;HUANG Jiashuang;JIANG Shu;WANG Haipeng(School of Artificial Intelligence and Computer Science,Nantong University,Nantong,Jiangsu 226019,China)
出处
《计算机科学》
北大核心
2025年第6期274-285,共12页
Computer Science
基金
国家重点研发计划(2024YFE0202700)
国家自然科学基金(62006128,62102199,62471259,62406153)
江苏省自然科学基金(BK20231337)
江苏省双创博士计划
江苏省高等学校自然科学研究面上项目(23KJB520031,24KJB520032)
南通市科技局基础科学研究项目(JC2021122)
中国博士后科学基金(2022M711716)
江苏省实践创新计划资助项目(SJCX24_2017)。
