Three-dimensional virtual-reality surgical planning and soft-tissue prediction for orthognathic surgery

Complex maxillofacial malformations continue to present challenges in analysis and correction beyond modern technology. The purpose of this paper is to present a virtual-reality workbench for surgeons to perform virtual orthognathic surgical planning and soft-tissue prediction in three dimensions. A resulting surgical planning system, i.e., three-dimensional virtual-reality surgical-planning and soft-tissue prediction for orthognathic surgery, consists of four major stages: computed tomography (CT) data post-processing and reconstruction, three-dimensional (3-D) color facial soft-tissue model generation, virtual surgical planning and simulation, soft-tissue-change preoperative prediction. The surgical planning and simulation are based on a 3-D CT reconstructed bone model, whereas the soft-tissue prediction is based on color texture-mapped and individualized facial soft-tissue model. Our approach is able to provide a quantitative osteotomy-simulated bone model and prediction of postoperative appearance with photorealistic quality. The prediction appearance can be visualized from any arbitrary viewing point using a low-cost personal-computer-based system. This cost-effective solution can be easily adopted in any hospital for daily use.published_or_final_versio

Federated Cross Learning for Medical Image Segmentation

Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with the data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple and yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a round-robin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code will be made publicly available after paper publication.Comment: 10 pages, 4 figure

Soft-tissue Driven Craniomaxillofacial Surgical Planning

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.Comment: Early accepted by MICCAI 202

Histopathology of Growth Anomaly Affecting the Coral, Montipora capitata: Implications on Biological Functions and Population Viability

Growth anomalies (GAs) affect the coral, Montipora capitata, at Wai'ōpae, southeast Hawai'i Island. Our histopathological analysis of this disease revealed that the GA tissue undergoes changes which compromise anatomical machinery for biological functions such as defense, feeding, digestion, and reproduction. GA tissue exhibited significant reductions in density of ova (66.1–93.7%), symbiotic dinoflagellates (38.8–67.5%), mesenterial filaments (11.2–29.0%), and nematocytes (28.8–46.0%). Hyperplasia of the basal body wall but no abnormal levels of necrosis and algal or fungal invasion was found in GA tissue. Skeletal density along the basal body wall was significantly reduced in GAs compared to healthy or unaffected sections. The reductions in density of the above histological features in GA tissue were collated with disease severity data to quantify the impact of this disease at the colony and population level. Resulting calculations showed this disease reduces the fecundity of M. capitata colonies at Wai'ōpae by 0.7–49.6%, depending on GA severity, and the overall population fecundity by 2.41±0.29%. In sum, GA in this M. capitata population reduces the coral's critical biological functions and increases susceptibility to erosion, clearly defining itself as a disease and an ecological threat

Partial inhibition of mitochondrial complex I ameliorates Alzheimer\u27s disease pathology and cognition in APP/PS1 female mice.

Alzheimer\u27s Disease (AD) is a devastating neurodegenerative disorder without a cure. Here we show that mitochondrial respiratory chain complex I is an important small molecule druggable target in AD. Partial inhibition of complex I triggers the AMP-activated protein kinase-dependent signaling network leading to neuroprotection in symptomatic APP/PS1 female mice, a translational model of AD. Treatment of symptomatic APP/PS1 mice with complex I inhibitor improved energy homeostasis, synaptic activity, long-term potentiation, dendritic spine maturation, cognitive function and proteostasis, and reduced oxidative stress and inflammation in brain and periphery, ultimately blocking the ongoing neurodegeneration. Therapeutic efficacy in vivo was monitored using translational biomarkers FDG-PET, 31P NMR, and metabolomics. Cross-validation of the mouse and the human transcriptomic data from the NIH Accelerating Medicines Partnership-AD database demonstrated that pathways improved by the treatment in APP/PS1 mice, including the immune system response and neurotransmission, represent mechanisms essential for therapeutic efficacy in AD patients