A Multi-Scale Collaborative Fusion Network for Joint Organ and Lesion Segmentation

Segmenting prostate organ areas and lesion areas based on multi-modal magnetic resonance images is an important task in the medical field, and has important reference value for doctors’ subsequent diagnosis and treatment plan formulation. However, existing segmentation methods still have some shortcomings. The most important thing is that many existing methods are mostly limited to the segmentation stage of a single task, which not only wastes computing resources, but also fails to make full use of shared features between different tasks and effectively utilize the spatial and texture structures of organs and lesions. Correlation can enhance the model’s understanding of anatomical structures and improve the accuracy and consistency of segmentation results. In this paper, we propose a multi-task learning model for multi-scale feature interaction and fusion (MFAS-Net), drawing on the insights of Progressive layered extraction model(PLE). The fusion module (MMFF) realizes cross-task feature interaction and fusion, thereby making better use of shared features between organs and lesions. The multi-scale attention supervision (MAS) module is used to supervise the lesions using feature maps of organ segmentation, and finally achieves joint segmentation of prostate organs and lesions. Through comparative experiments and ablation experiments on the PI-CAI data set, our model showed significant superiority in the joint segmentation task of prostate organs and lesions, verifying its effectiveness and practicability in medical image segmentation.