Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hindering practical applications. To address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet. Our method begins with non-destructive 3D imaging of grains using micro-CT. For the accurate segmentation of kernels and chalky regions, we propose VSE-UNet, an improved VGG-UNet with an SE attention mechanism for enhanced feature learning. Through comprehensive training optimization strategies, including the Dice focal loss function and dropout technique, the model achieves robust and accurate segmentation of both kernels and chalky regions in continuous CT slices. To enable high-throughput 3D analysis, we developed a unified 3D detection framework integrating isosurface extraction, point cloud conversion, DBSCAN clustering, and Poisson reconstruction. This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. Finally, 3D morphological indicators of chalkiness are calculated using triangular mesh techniques. Experimental results demonstrate significant improvements in both 2D segmentation (7.31% improvement in chalkiness IoU, 2.54% in mIoU, 2.80% in mPA) and 3D phenotypic measurements, with VSE-UNet achieving more accurate volume and dimensional measurements compared with the baseline. These improvements provide a reliable foundation for studying chalkiness formation and enable high-throughput phenotyping.