Bailoni, A (2021). Deep Learning for Graph-Based Image Instance Segmentation. Heidelberg University

 Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, Vilches-Barro, A, Louveaux, M, Wenzel, C, Strauss, S, Wilson-Sanchez, D, Lymbouridou, R, Steigleder, S S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Tsiantis, M, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020). Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution. eLife. 9

 Kirschbaum, E, Bailoni, A and Hamprecht, F A (2020). DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. MICCAI. Proceedings. 151-162

 Bailoni, A, Pape, C, Wolf, S, Kreshuk, A and Hamprecht, F A (2020). Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR. Springer. 12544 331-344

 Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020). The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV. Proceedings. 208-224

 Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image   Partitioning. ECCV. Proceedings. Springer. 571-587