Cardiac scarring and edema are critical pathological features of heart diseases. Accurate segmentation of these features in Cardiac Magnetic Resonance (CMR) imaging is crucial for understanding the pathological changes associated with cardiac diseases. In the field of myocardial scar and edema segmentation, it is of significant importance to study the C0, T2, and LGE modalities. These modalities offer different perspectives on myocardial tissue characteristics, aiding in the more accurate diagnosis and assessment of cardiac diseases. However, the high-intensity features of scars and edema cannot be directly obtained from individual CMR imaging sequences, making simultaneous accurate segmentation challenging. To address this, we propose a multi-modal, multi-channel fusion interactive progressive segmentation strategy that leverages the distinctive properties of each modality and the surrounding tissue characteristics for the segmentation of myocardial scars and edema. We have designed a multi-channel fusion interactive progressive segmentation model, suitable for scar and myocardial segmentation, which incorporates an attention mechanism that enhances channel information interaction within a U-Net structure to extract features across different modalities. On the MyoPS++ 2024 public dataset, our method achieved an average Dice score of 0.5486 for scar segmentation and 0.6081 for the segmentation of both scars and edema.