Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation.

Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons.

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD.

Ultrastructure and gene mapping of the albino mutant al12 in rice (Oryza sativa L.).

Seedling albino mutation resistant to low temperature is an adaptability of rice (Oryza sativa L.) to cold. The mutant, a conditional expression controlled by development and temperature, differs from other albino mutants.