期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer
1
作者 Mohammed A.Mahdi Shahanawaj Ahamad +3 位作者 Sawsan A.Saad Alaa Dafhalla Alawi Alqushaibi Rizwan Qureshi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第12期2351-2373,共23页
The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment p... The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging. 展开更多
关键词 PET/ct imaging tumor segmentation weighted fusion transformer multi-modal imaging deep learning neural networks clinical oncology
下载PDF
基于小波统计特性的CT/MR医学影像优化融合方法
2
作者 王淑 王恒山 +1 位作者 孙迎 肖刚 《计算机工程与应用》 CSCD 北大核心 2006年第17期212-214,共3页
多模医学影像融合技术服务于临床诊断具有十分重要的意义。计算机断层摄像CT(ComputerTomography)仅能清晰显示人体骨骼组织,而磁共振成像MR(MagneticResonance)具有软组织对比分辨率高的特点。论文提出了一种基于小波统计特性的CT、MR... 多模医学影像融合技术服务于临床诊断具有十分重要的意义。计算机断层摄像CT(ComputerTomography)仅能清晰显示人体骨骼组织,而磁共振成像MR(MagneticResonance)具有软组织对比分辨率高的特点。论文提出了一种基于小波统计特性的CT、MR医学颅脑部影像优化融合方法,采用信息熵和边缘保持度两项指标作为优化融合依据,获得的融合影像有效地综合CT与MR影像信息,可同时清晰地显示脑部骨组织和软组织信息。 展开更多
关键词 多模医学影像 优化融合 ct mr
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部