文献类型：会议论文

英文题名：MS-TFGNet: A Dual-Branch Transformer for Guided Learning in Lung Cancer Lesion Segmentation Using PET/CT Images

作者：Mao, Junfeng[1,2]; Wu, Yusheng[3,3]; Xiang, Hongqin[3,4]; Wang, Wan[2]; Ha, Xiaoqin[1,5]; Wei, Jingjun[2]

第一作者：Mao, Junfeng

机构：[1] School of Basic Medical Sciences, Gansu University of Chinese Medicine, Lanzhou, China; [2] 940th Hospital of the Joint Logistics Support Force, The Chinese People's Liberation Army, Department of Nuclear Medicine, Lanzhou, China; [3] Northwest Minzu University, Key Laboratory of China's Ethnic Languages and Information Technology, Ministry of Education, Lanzhou, China; [4] Northwest Minzu University, Key Laboratory of Computational Nuclear Medicine, Lanzhou, China; [5] 940th Hospital of the Joint Logistics Support Force, The Chinese People's Liberation Army, Department of Clinical Laboratory Medicine, Lanzhou, China

第一机构：甘肃中医药大学基础医学院（敦煌医学研究所）

通信机构：[1]School of Basic Medical Sciences, Gansu University of Chinese Medicine, Lanzhou, China|[107351d2d02a88e1f325f]甘肃中医药大学基础医学院（敦煌医学研究所）;[10735]甘肃中医药大学;

会议论文集：2024 5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

会议日期：October 18, 2024 - October 20, 2024

会议地点：Hangzhou, China

语种：英文

外文关键词：Biological organs - Computerized tomography - Diagnosis - Diseases - Image segmentation - Positron emission tomography

年份：2024

摘要：Lung cancer is the most common and lethal malignant tumor globally, characterized by a tendency for local invasion and distant metastasis, which complicates detection. Thus, accurate lesion segmentation for lung cancer is crucial. Positron emission tomography-computed tomography (PET/CT) provides both anatomical information and metabolic information about tissues in the clinical diagnosis of lung cancer. However, existing PET/CT -based multimodal segmentation strategies typically achieve segmentation by concatenating and fusing the features extracted from both modalities. These approaches often overlook the complementary nature of the feature extraction process between the different modalities. To address these issues, we propose an end-to-end deep learning architecture, called the Multi-scale Transformer Feature Fusion and Guided Network (MS-TFGNet), aimed at improving the accuracy of lung cancer lesion segmentation using PET/CT images. Specifically, a dual-branch structure was designed to integrate multimodal features, the PET provides metabolic information to guide the CT branch using a Multi-modal Cross-Attention Transformer (MCA) module during training. Finally, MS-TFGNet was validated on a real clinical PET/CT dataset, achieving a DSC, precision, and recall of 0.8502, 0.8401, and 0.8610, respectively. ? 2024 IEEE.