We propose a data augmentation method to improve thesegmentation accuracy of the convolutional neural network on multi-modality cardiac magnetic resonance (CMR) dataset. The strategy aims to reduce over-fitting of the network toward any specific intensity or contrast of the training images by introducing diversity in these two aspects. The style data augmentation (SDA) strategy increases the size of the training dataset by using multiple image processing functions including adaptive histogram equalisation, Laplacian transformation, Sobel edge detection, intensity inversion and histogram matching. For the segmentation task, we developed the thresholded connection layer network (TCL-Net), a minimalist rendition of the U-Net architecture, which is designed to reduce convergence and computation time. We integrate the dual U-Net strategy to increase the resolution of the 3D segmentation target. Utilising these approaches on a multi-modality dataset, with SSFP and T2 weighted images as training and LGE as validation, we achieve 90% and 96% validation Dice coefficient for endocardium and epicardium segmentations. This result can be interpreted as a proof of concept for a generalised segmentation network that is robust to the quality or modality of the input images. When testing with our mono-centric LGE image dataset, the SDA method also improves the performance of the epicardium segmentation, with an increase from 87% to 90% for the single network segmentation.