This paper presents a novel solution for catastrophic forgetting in life long learning (LL) using Dynamic ConvolutionNeural Network (Dy-CNN). The proposed dynamic convolution layer, can adapt convolution filters bylearning kernel coefficients or weights based on the input image. Suitability of the proposed Dy-CNN in a lifelongsequential learning-based scenario with multi-modal MR images is experimentally demonstrated for segmentation of Glioma tumor from multi-modal MR images. Experimental results demonstrated the superiority of the Dy-CNN-based segmenting network in terms of learning through multi-modal MRI images and better convergence of lifelong learning-based training.