For images with bimodal gray-level histogram, the traditional two-dimensional histogram threshold segmentation method is more effective, but when gray-level histogram is non-peak, unimodal or multimodal, their segmentation results are poor. Considering that the two-dimensional survival function obtained by two-dimensional histogram mapping has the advantages of continuous density and uniform morphology, a fast two-dimensional cumulative residual Tsallis entropy threshold segmentation method is proposed based on the two-dimensional survival function of images. The method firstly constructs a two-dimensional survival function based on the two-dimensional histogram, and then a two-dimensional cumulative residual Tsallis entropy objective function is defined to compute the segmentation threshold on the basis of the two-dimensional survival function. Further, a recursive algorithm is used to reduce time complexity of calculating the objective function to O(L2). Finally, based on the two-dimensional cumulative residual Tsallis entropy criterion in recursive form, an optimal threshold vector is obtained for threshold segmentation. In 26 synthetic images and 76 real-world images, the proposed method is compared with two fast two-dimensional threshold segmentation methods, two clustering segmentation methods and one active contour segmentation method respectively under two indicators of time and misclassification error (ME). Experimental results show that the time is shortened by 0.013 s, and ME value is reduced by 0.051~0.089 on average in comparison with the method of performance 2 in both synthetic and real-world images. The proposed fast two-dimensional cumulative residual Tsallis entropy threshold segmentation method is not only superior to the 5 comparison methods in computational efficiency, but also has relatively obvious advantages in segmentation adaptability and segmentation accuracy.