We introduce a deep learning pipeline for segmenting apical stem cells in three-dimensional confocal microscopy volumes of Arabidopsis thaliana. By integrating pre-trained 2D and 3D U-Net models from the BioImage Model Zoo, our method combines in-plane boundary sharpness with volumetric continuity. The workflow encompasses parallel preprocessing, dual-model inference, logical fusion, 3D reconstruction, and membrane-aware post-processing to extract key morphometric features, including cell counts and volumes. Evaluated over 22 timepoints, our pipeline achieves a mean Dice similarity coefficient (DSC) of \(0.886\,\pm \,0.002\) . Passing–Bablok regression on cell counts yields a slope of 0.885 ( \(p = 0.573\) ), surpassing the standalone 2D U-Net (0.539) and 3D U-Net (0.782). Volume estimates exhibit a slope of 0.754 ( \(p = 0.107\) ), with smaller cell volumes but substantially fewer cell-merging artifacts compared to the baselines (0.738 and 0.930, respectively). These results highlight the advantages of model fusion for robust, biologically meaningful segmentation. The complete pipeline and source code are publicly available at: https://github.com/GolpedeRemo37/Arabidopsis_DualDL