RESUMEN
Natural Coral Particles (NCPs) are a suitable scaffold material for Guided Bone Regeneration (GBR) procedures; it combines the placement of a bone substitute supporting a barrier membrane. Due to increasing sea pollution and the declarations of endangered coral species (KYOTO 1997), they are no longer suitable for the medical industry. Novel domestic corals have been grown under controlled conditions to produce cultivated coral graft (CCG) material. This study aimed to evaluate a new CCG in an in vivo experimental GBR procedure. The calvarias of 8 rabbits were surgically exposed, and circular defects 8 mm in diameter were prepared. One defect was filled with CCG particles (experimental group); the contralateral defect (control group) was spontaneously filled by blood clot. The defects were covered with a collagen membrane. Animals were euthanized after 8 weeks. Histological observations of the defects showed similar bone growth patterns in both experimental and control osteotomies. In the experimental defects, no traces of coral particles were observed. Histometric analysis showed denser bone in the pristine zone (65-66%) than in the peripheral zone for both the control (50%) and experimental defects (31%) (P= NS). The new bone percentage was reduced from the peripheral zone toward the middle and the center of the defect (31%, 32% and 27%, respectively) as the distance from the peripheral pristine bone borders increased. The existing data support the complete degradation of CCG as space-maintaining scaffold for GBR procedures.
RESUMEN
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
