Root system architecture (RSA) plays a pivotal role in efficient uptake of essential nutrients, such as phosphorous (P), nitrogen (N), and water. In soils with heterogeneous nutrient distribution, root plasticity can optimize acquisition and plant growth. Here, we present evidence that a constitutive RSA can confer benefits for sorghum grown under both sufficient and limiting growth conditions. Our studies, using P efficient SC103 and inefficient BTx635 sorghum cultivars, identified significant differences in root traits, with SC103 developing a larger root system with more and longer lateral roots, and enhanced shoot biomass, under both nutrient sufficient and deficient conditions. In addition to this constitutive attribute, under P deficiency, both cultivars exhibited an initial increase in lateral root development; however, SC103 still maintained the larger root biomass. Although N deficiency and drought stress inhibited both root and shoot growth, for both sorghum cultivars, SC103 again maintained the better performance. These findings reveal that SC103, a P efficient sorghum cultivar, also exhibited enhanced growth performance under N deficiency and drought. Our results provide evidence that this constitutive nature of RSA can provide an avenue for breeding nutrient- and drought-resilient crops.

X-ray micro-computed tomography (μCT) is widely used in biomedical research for non-destructive, high-resolution imaging. Synchrotron Radiation Phase-Contrast μCT (SR-PCI-μCT) further enhances image quality with higher Signal to Noise Ratio (SNR), improved contrast, and faster acquisition. However, segmenting SR-PCI-μCT images remains challenging due to their heterogeneous image property and limited training data. Additionally, the increasing throughput of synchrotron facilities demands robust, efficient segmentation methods. This paper reviews a list of recent segmentation approaches in biomedical μCT. Traditional methods remain simple and effective for simple, high-contrast structures but require extensive tuning and generalize poorly to complex, low-contrast soft tissues. Data-driven models provide higher accuracy and robustness yet rely heavily on large expert-annotated datasets, limiting reproducibility and cross-dataset adaptability. Recent advance on vision transformers have shifted the paradigm from task-specified to more domain-specified segmentation, though these techniques are still evolving and require adaptation for SR-PCI-μCT studies. This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μCT segmentation. It consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI-μCT. Additionally, it outlines open challenges to guide future research and practical advancements in biomedical μCT segmentation.