The study of genetic and molecular mechanisms underlying tissue morphogenesishas received a lot of attention in biology. Especially, accurate segmentation oftissues into individual cells plays an important role for quantitative analyzing thedevelopment of the growing organs. However, instance cell segmentation is stilla challenging task due to the quality of the image and the fine-scale structure.Any small leakage in the boundary prediction can merge different cells together,thereby damaging the global structure of the image. In this paper, we propose anend-to-end topology-aware 3D segmentation method for plant tissues. The strengthof the method is that it takes care of the 3D topology of segmented structures. Our method relies on a common deep neural network. The keystone is a trainingphase and a new topology-aware loss - the CavityLoss - that are able to help thenetwork to focus on the topological errors to fix them during the learning phase.The evaluation of our method on both fixed and live plant organ datasets shows thatour method outperforms state-of-the-art methods (and contrary to state-of-the-artmethods, does not require any post-processing stage). The code of CavityLoss isfreely available at https://xxxxxxxxxxxxxxxxxxxxxxxxx.