Enhancing endoscopic scene reconstruction with color-aware inverse rendering through neural SDF and radiance fields.

Virtual surgical training is crucial for enhancing minimally invasive surgical skills. Traditional geometric reconstruction methods based on medical CT/MRI images often fall short in providing color information, which is typically generated through pseudo-coloring or artistic rendering. To simultaneously reconstruct both the geometric shape and appearance information of organs, we propose a novel organ model reconstruction network called Endoscope-NeSRF. This network jointly leverages neural radiance fields and Signed Distance Function (SDF) to reconstruct a textured geometric model of the organ of interest from multi-view photometric images acquired by an endoscope. The prior knowledge of the inverse correlation between the distance from the light source to the object and the radiance improves the real physical properties of the organ. The dilated mask further refines the appearance and geometry at the organ's edges. We also proposed a highlight adaptive optimization strategy to remove highlights caused by the light source during the acquisition process, thereby preventing the reconstruction results in areas previously affected by highlights from turning white. Finally, the real-time realistic rendering of the organ model is achieved by combining the inverse rendering and Bidirectional Reflectance Distribution Function (BRDF) rendering methods. Experimental results show that our method closely matches the Instant-NGP method in appearance reconstruction, outperforming other state-of-the-art methods, and stands as the superior method in terms of geometric reconstruction. Our method obtained a detailed geometric model and realistic appearance, providing a realistic visual sense for virtual surgical simulation, which is important for medical training.

Pulmonary nodule detection on lung parenchyma images using hyber-deep algorithm.

The incidence of lung cancer has seen a significant increase in recent times, leading to a rise in fatalities. The detection of pulmonary nodules from CT images has emerged as an effective method to aid in the diagnosis of lung cancer. Ensuring information security holds utmost significance in the detection of nodules, with particular attention given to safeguarding patient privacy within the context of the Internet of Things (IoT). In this regard, migration learning emerges as a potent technique for preserving the confidentiality of patient data. Firstly, we applied several data-preprocessing steps such as lung segmentation based on K-Means, denoising methods, and lung parenchyma extraction through a dedicated medical IoT network. We used the Microsoft Common Object in Context (MS-COCO) dataset to pre-train the detection framework and fine-tuned it with the Lung Nodule Analysis 16 (LUNA16) dataset to adapt to nodule detection tasks. To evaluate the effectiveness of our proposed pipeline, we conducted extensive experiments that included subjective evaluation of detection results and quantitative data analysis. The results of these experiments demonstrated the efficacy of our approach in accurately detecting pulmonary nodules. Our study provides a promising framework for trustworthy pulmonary nodule detection on lung parenchyma images using a secured hyper-deep algorithm, which has the potential to improve lung cancer diagnosis and reduce fatalities associated with it.

Detection of binocular chromatic fusion limit for opposite colors.

When the input colors of the left and right eyes are different from one another, binocular rivalry may occur. According to Hering theory, opponent colors would have the most significant tendency for rivalry. However, binocular color fusion still occurs under the condition that each eye's opponent chromatic responses do not exceed a specific chromatic fusion limit (CFL). This paper detects the binocular chromatic fusion limit for opposite colors within a conventional 3D display color gamut. We conducted a psychophysical experiment to quantitatively measure the binocular chromatic fusion limit on four opposite color directions in the CIELAB color space. Due to color inconsistency between eyes may affect the binocular color fusion, the experiment was divided into two sessions by swapping stimulation colors of left and right eyes. There were 5 subjects and they each experienced 320 trials. By analyzing the results, we used ellipses to quantify the chromatic fusion limits for opposing colors. The average semi-major axis of the ellipses is 27.55 Δ E a b∗, and the average semi-minor axis is 16.98 Δ E a b∗. We observed that the chromatic fusion limit varies with the opposite color direction: the CFL on RedBlue-GreenYellow direction is greater than that on Red-Green direction, the latter being greater than that on Yellow-Blue direction and the CFL on RedYellow-GreenBlue direction is smallest. Furthermore, we suggested that the chromatic fusion limit is independent of the distribution of cells, and there is no significant change in the fusion ellipse boundaries after swapping left and right eye colors.

Automatically Addressing System for Ultrasound-Guided Renal Biopsy Training Based on Augmented Reality.

Chronic kidney disease has become one of the diseases with the highest morbidity and mortality in kidney diseases, and there are still some problems in surgery. During the operation, the surgeon can only operate on two-dimensional ultrasound images and cannot determine the spatial position relationship between the lesion and the medical puncture needle in real-time. The average number of punctures per patient will reach 3 to 4, Increasing the incidence of complications after a puncture. This article starts with ultrasound-guided renal biopsy navigation training, optimizes puncture path planning, and puncture training assistance. The augmented reality technology, combined with renal puncture surgery training was studied. This paper develops a prototype ultrasound-guided renal biopsy surgery training system, which improves the accuracy and reliability of the system training. The system is compared with the VR training system. The results show that the augmented reality training platform is more suitable as a surgical training platform. Because it takes a short time and has a good training effect.

Augmented reality in neurosurgical navigation: a survey.

BACKGROUND: Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyze the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems. METHODS: In this survey, we searched related keywords "augmented reality", "virtual reality", "neurosurgery", "surgical simulation", "brain tumor surgery", "neurovascular surgery", "temporal bone surgery", and "spinal surgery" through Google Scholar, World Neurosurgery, PubMed and Science Direct. We collected 85 articles published over the past five years in areas related to this survey. RESULTS: Detailed study has been conducted on the application of AR in neurosurgery and found that AR is constantly improving the overall efficiency of doctor training and treatment, which can help neurosurgeons learn and practice surgical procedures with zero risks. CONCLUSIONS: Neurosurgical navigation is essential in neurosurgery. Despite certain technical limitations, it is still a necessary tool for the pursuit of maximum security and minimal intrusiveness. This article is protected by copyright. All rights reserved.

Topological supersolidity of dipolar Fermi gases in a spin-dependent optical lattice.

We investigate the topological supersolid states of dipolar Fermi gases trapped in a spin-dependent 2D optical lattice. Our results show that topological supersolid states can be achieved via the combination of topological superfluid states with the stripe order. Different from the general held belief that supersolid state in fermionic system can only survive with simultaneous coexistence of the repulsive and attractive dipolar interaction. We demonstrate that it can be maintained when the dipolar interaction is attractive in both x and y direction. By adjusting the ratio of hopping amplitude between different directions and dipolar interaction strength U, the system will undergo a phase transition among p x + ip y superfluid state, p y -wave superfluid state, and the topological supersolid state. The supersolid state in the attractive environment is proved to be stable by the positive sign of the inverse compressibility. We also design an experimental protocol to realize the staggered next-next-nearest-neighbor hopping via the laser assisted tunneling technique, which is the key to simulate the spin-dependent potential.

Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data.

Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared to the original intraoperative data, there has always been an argument that these data are represented by lower fidelity in virtual surgical training. In this paper, a dynamic biomechanics experimental framework is designed to achieve a highly immersive haptic sensation during the biopsy therapy with human respiratory motion; it is the first time to introduce the idea of periodic extension idea into the dynamic percutaneous force modeling. Clinical evaluation is conducted and performed in the Yunnan First People's Hospital, which not only demonstrated a higher fitting degree (AVG: 99.36%) with the intraoperation data than previous algorithms (AVG: 87.83%, 72.07%, and 66.70%) but also shows a universal fitting range with multilayer tissue. 27 urologists comprising 18 novices and 9 professors were invited to the VR-based training evaluation based on the proposed haptic rendering solution. Subjective and objective results demonstrated higher performance than the existing benchmark training simulator. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled medical simulation systems would be able to provide a more immersive and effective training environment.

Towards Virtual VATS, Face, and Construct Evaluation for Peg Transfer Training of Box, VR, AR, and MR Trainer.

The aim of this study is to develop and assess the peg transfer training module face, content and construct validation use of the box, virtual reality (VR), cognitive virtual reality (CVR), augmented reality (AR), and mixed reality (MR) trainer, thereby to compare advantages and disadvantages of these simulators. Training system (VatsSim-XR) design includes customized haptic-enabled thoracoscopic instruments, virtual reality helmet set, endoscope kit with navigation, and the patient-specific corresponding training environment. A cohort of 32 trainees comprising 24 novices and 8 experts underwent the real and virtual simulators that were conducted in the department of thoracic surgery of Yunnan First People's Hospital. Both subjective and objective evaluations have been developed to explore the visual and haptic potential promotions in peg transfer education. Experiments and evaluation results conducted by both professional and novice thoracic surgeons show that the surgery skills from experts are better than novices overall, AR trainer is able to provide a more balanced training environments on visuohaptic fidelity and accuracy, box trainer and MR trainer demonstrated the best realism 3D perception and surgical immersive performance, respectively, and CVR trainer shows a better clinic effect that the traditional VR trainer. Combining these in a systematic approach, tuned with specific fidelity requirements, medical simulation systems would be able to provide a more immersive and effective training environment.

Combination of twelve alleles at six quantitative trait loci determines grain weight in rice.

Grain weight, which is controlled by quantitative trait loci (QTLs), is one of the most important determinants of rice yield. Although many QTLs for grain weight have been identified, little is known about how different alleles in different QTLs coordinate to determine grain weight. In the present study, six grain-weight-QTLs were detected in seven mapping populations (two F2, one F3, and four recombinant inbred lines) developed by crossing 'Lemont', a United States japonica variety, with 'Yangdao 4', a Chinese indica variety. In each of the six loci, one allele from one parent increased grain weight and one allele from another parent decreased it. Thus, the 12 alleles at the six QTLs were subjected to regression analysis to examine whether they acted additively across loci leading to a linear relationship between the predicted breeding value of QTL and phenotype. Results suggested that a combination of the 12 alleles determined grain weight. In addition, plants carrying more grain-weight-increasing alleles had heavier grains than those carrying more grain-weight-decreasing alleles. This trend was consistent in the seven mapping populations. Thus, these six QTLs might be used in marker-assisted selection of grain weight, by stacking different grain-weight-increasing or -decreasing alleles.

Genotype by Environment Interaction: The Greatest Obstacle in Precise Determination of Rice Sheath Blight Resistance in the Field.

Rice sheath blight (SB) is the most serious rice disease in China. Resistance of rice to SB is a quantitative trait that is easily influenced by the environment; however, the extent of environmental influence on SB field resistance is still poorly understood. To identify rice genotype by environment interactions for SB resistance, 211 rice genotypes originating from 15 countries were planted and evaluated for SB field resistance in six different environments between 2012 and 2016 after inoculation with the SB pathogen isolate ZJ03. In addition, 65 rice genotypes were evaluated for SB field resistance in another four environments between 2013 and 2016 using ZJ03. Variations in SB field resistance were observed in different genotypes in different environments using objective and subjective rating methods. Two-way analysis of variance indicated that the interaction between the genotype and environment had a highly significant effect on SB field resistance. This analysis indicated that the environment had more of an influence than the genotype itself on SB field resistance, and the genotype by environment interaction was the greatest obstacle in obtaining a precise determination of SB field resistance in rice. The most resistant genotype, GD66, is a good candidate for genetic studies and breeding.