Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network  

作　　者：Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang 

机构地区：[1]School of Computer Science and Engineering,North Minzu University,Yinchuan,750021,China [2]School of Medical Information&Engineering,Ningxia Medical University,Yinchuan,750004,China [3]Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission,NorthMinzuUniversity,Yinchuan,750021,China

出　　处：《Computers, Materials & Continua》2024年第9期4813-4832,共20页计算机、材料和连续体（英文）

基　　金：supported in part by the National Natural Science Foundation of China(Grant No.62062003);Natural Science Foundation of Ningxia(Grant No.2023AAC03293).

摘　　要：Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.

关 键 词：Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement 

分 类 号：R73[医药卫生—肿瘤] TP39[医药卫生—临床医学]