MambaYOLACT: you only look at mamba prediction head for head-neck lymph nodes

Abstract

Lymph nodes in the head-neck are often infected when malignant tumors metastasize. At present, Magnetic Resonance Imaging (MRI) is widely used in the evaluation of head-neck lymph nodes. However, there are some problems, such as different sizes, low contrast of head-neck lymph nodes. The instance segmentation accuracy of head-neck lymph nodes is decreased, which affects the patients treatment decision and the surgical effect evaluation. To solve these problems, a single stage Mamba YOLACT instance segmentation model is proposed in this paper. The main contributions are as follows: Firstly, a Cross-field and Cross-direction Feature Enhancement module (CCFE) is designed. The module through the channel grouping mechanism, effectively enhances the ability of each group of features to express different spatial semantic information, by mixing attention mechanism to improve the feature extraction ability of lesions with different dimensions. Secondly, a MambaNet-based prediction head module is designed. The module combined the State-Space Model (SSM) and self-attention mechanism to accurately capture global image dependencies, highlight the lesion area. Thirdly, A dataset of MRI images of head-neck lymph nodes is used to verify the model effectiveness. The results show that the values of APdet, APseg, ARdet, ARseg, mAPdet and mAPseg are 69.8%, 70.9%, 55.3%, 56.4%, 39.4% and 41.0%, respectively. The model can achieve accurate segmentation of the lymph nodes, which has positive significance for lymph nodes auxiliary diagnosis.