Segmentation network for metastatic lymph nodes of head and neck tumors
Head and neck tumors are prevalent malignant tumors in China,with prognosis significantly in-fluenced by cervical lymph node metastasis.In medical practice,magnetic resonance imaging(MRI)is em-ployed to identify metastatic lymph nodes.However,MRI images often suffer from blurred edges and low contrast between the lesion and surrounding tissue.This paper introduces a segmentation network tailored for metastatic lymph nodes in head and neck tumors.Initially,a cross-layer and cross-field attention mod-ule is developed,integrating features from both deep and shallow layers to enhance the shape representa-tion of metastatic lymph nodes through a self-attention mechanism.This module improves contextual se-mantic understanding across different receptive fields,allowing for pixel-level fusion of shallow and deep feature maps,thereby enhancing the morphological details of metastatic lymphatic nodes.Subsequently,a multi-scale feature fusion module is designed to amalgamate features across various scales in the feature pyramid,enriching the morphological details of the lymph nodes.Furthermore,an enhanced attention pre-diction head module is implemented,combining parallel self-attention and gate channel transformation to accentuate the lesion area and refine its boundaries on the feature map.The network's effectiveness is con-firmed using a clinical dataset of lymph node metastasis medical images.The performance metrics,AP-det,APseg,ARdet,ARseg,mAPdet,and mAPseg for lymph node metastasis lesion segmentation are 74.88%,74.12%,63.11%,62.28%,74.64%,and 74.04%,respectively.This network provides pre-cise detection and segmentation of lymph node metastasis lesions,offering significant benefits for lymph node diagnosis.
medical image processinghead and neck tumorslymph node metastasisinstance segmen-tationattention mechanism
万方数据
维普
