Abstract: In order to achieve accurate segmentation of the optic cup and optic disc of fundus images and to reduce the uncertainty and time-consuming nature of manual segmentation methods, a novel convolutional neural network for joint optic cup and optic disc segmentation, called M2DS-TransUNet, is proposed in this paper. This network adopts a multi-resolution image combination and adaptive extraction of the input form through the squeeze and excitation modules. It also combines the advantages of multi-resolution module, Transformer and depth supervision, which allows the network to extract richer image information. The network model is trained using a five-fold cross-validation approach and experimentally validated and evaluated on three current mainstream datasets REFUGE, DRISHTI-GS and RIM-ONE-r3, which achieve 0.0284, 0.0978 and 0.0179 respectively in the cup-to-disc ratio index that best reflects the segmentation effect, and its segmentation effect is better than some current classical algorithms.The experimental results show that the proposed method can extract richer information of the visual cup-vision disc and has the ability of generalization across data sets, which is a very competitive method for joint optic cup and optic disc segmentation.