In the field of medical imaging, many different image modalities contain different information, helping practitionners to make diagnostic, follow-up, etc. To better analyze images, mixing multi-modalities information has become a trend. This paper provides one cascaded UNet framework and uses three different modalities (the late gadolinium enhancement (LGE) CMR sequence, the balanced- Steady State Free Precession (bSSFP) cine sequence and the T2-weighted CMR) to complete the segmentation of the myocardium, scar and edema in the context of the MICCAI 2020 myocardial pathology segmentation combining multi-sequence CMR Challenge dataset (MyoPS 2020). We evaluate the proposed method with 5-fold-cross-validation on the MyoPS 2020 dataset.