The applications of hypercomplex algebras, such as complex numbers and quaternions, to machine learning as an alternative to real-valued architectures have been an established area of research. Clifford algebra can be viewed as a structure generalising hypercomplex number systems. More recently, Clifford algebra has attracted attention in the context of neural networks, with applications for example in the areas of partial differential equation modelling or image analysis. In neural networks, the choice of a particular Clifford algebra used in the architecture along with the embedding of input data into the algebra can be thought of as a hyperparemeter of the model. In this article, we investigate the applications of Clifford algebra to multidimensional medical imaging data through a modification of the U-Net architecture at the embedding level. We selected the breast cancer segmentation as a case study, considering dynamic contrast-enhanced magnetic resonance images (DCE-MRI). This acquisition consists of a sequence of MRI, in which MRI is acquired after the administration of a contrast agent. Such multidimensional input was analysed by modifying the traditional U-Net architecture integrating a Clifford algebra block to exploit correlations between MRI timestamps.