High-definition Images Research Articles

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer.

To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging. Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings. These embeddings served as input for a standard support vector machine (SVM) to classify the tissues as neoplastic or normal. The model's discriminative performance was validated using an 80-20 train-validation split. From 38 endoscopic NBI videos, 327 image patches were analyzed. The classification results in the validation cohort demonstrated high accuracy (92%) and precision (89%), with a perfect recall (100%) and an F1-score of 94%. The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.96. The use of large vision model-derived embeddings effectively differentiated between neoplastic and normal oropharyngeal tissues. This study supports the feasibility of employing CV foundation models like DINOv2 in the endoscopic evaluation of mucosal lesions, potentially augmenting diagnostic precision in Otorhinolaryngology. 4 Laryngoscope, 134:4535-4541, 2024.

Enhanced Safety and Efficacy of O-Arm Navigation Over C-Arm Guidance in Percutaneous Kyphoplasty for Patients With Osteoporotic Vertebral Compression Fractures and Spinal Deformity: A Comparative Study.

Although percutaneous kyphoplasty (PKP) under C-arm guidance is an effective treatment for osteoporotic vertebral compression fractures (OVCF), obtaining high-definition images in patients with OVCF and spinal deformities can be challenging or insufficient using traditional C-arm guidance, prompting our institution to adopt the O-arm navigation system-which offers comprehensive 3D imaging and precise navigation-and this study compares its safety and efficacy with conventional C-arm-assisted PKP. This was a retrospective study. From February 2019 to February 2022, we enrolled 28 patients with OVCF (44 vertebrae) with spinal deformity treated with O-arm navigation-assisted PKP and 30 patients with OVCF (42 vertebrae) with spinal deformity treated with C-arm-guided PKP. We recorded puncture times, single-segment operation time, number of cases with bone cement leakage, and length of stay. The visual analog scales (VASs), Oswestry disability indexes (ODIs), recovery of Cobbs angle, and vertebral height were used to assess treatment effect before the operation, on the first day postoperation, the first month postoperation, and at the final follow-up. The chi-squared test was utilized for comparing discrete variables, an independent samples t-test was used for continuous variables, and Pearson's chi-squared test and Fisher's exact test were applied for categorical data. Demographic features were comparable between the groups. The O-arm navigation group showed a significant reduction in puncture adjustment per vertebrae, single-segment operation time, and the rate of trocar needle malposition compared to the C-arm guidance group. The rate of cement leakage was decreased in the O-arm-guided PKP group, and other complications did not differ between the two groups. Intragroup comparisons revealed significant improvements in VAS scores and ODI on the first day, first month, and final follow-up after the operation (p < 0.05). The VAS score was significantly lower in the O-arm navigation-assisted PKP group than in the C-arm-guided PKP group on the first day postoperatively (p = 0.049). However, no significant differences in VAS scores were observed between the groups at the first month postoperatively or at the final follow-up. In each follow-up period, there was no significant difference in ODI, Cobb angle, and the percent of anterior vertebral height (AVH %) between the groups. O-arm navigation-assisted PKP demonstrates better clinical safety and efficacy than C-arm-guided PKP, marking it as a minimally invasive, safe, and effective procedure for treating patients with OVCF with spinal deformity.

Development characteristics and hazard analysis of debris flow along the Emei to Mianning section of the Chengdu–Kunming Railway

Geological disasters in the Emei to Mianning section of the Chengdu–Kunming Railway located in southwestern China occur frequently, and many debris flow gullies are distributed along the line. Debris flows often cause damage to the railway and highway that threatens the safe operation of the original line and the double-track line. Along the railway, the debris flow outbreak risks are analyzed for 53 debris flow gullies using field survey, remote sensing interpretation, and numerical simulation. Their developmental characteristics were judged according to the code. The results are used for numerical simulation of debris flow to further determine the movement characteristics and the accumulation range of debris flow. The results are combined with high-definition satellite images to analyze the form of the railway passing from the mouth of the ditch and the simulation results to determine the impact on the safe operation of the Chengdu–Kunming Railway when the debris flow erupts. Finally, disaster prevention and mitigation projects are proposed, and the prevention and control effects of the proposed works are validated.

New Frontiers in Pediatric Cochlear Implant Surgery - A Single Center Experience with the 3-Dimensional Exoscope.

Since its introduction by Wullstein, the binocular surgical microscope has remained the gold standard of visualization in the field of otology. However, in the last decade, new technology became available in the form of the three-dimensional (3D) exoscope. In this article, we describe our experience thus far in pediatric cochlear implantation with the 3D exoscope. This article is about prospective descriptive study of all exoscopic cochlear implant (CI) cases in a quaternary pediatric CI center performed with the Vitom® 3D system. All pediatric patients (age <18) were included without exclusion criteria, and our experience and conversion to microscope rates are reported. Since the introduction of the exoscope to our unit, we have successfully performed 68 cases, of which 53 were bilateral cochlear implantations. The age of the patient varied between 10 months and 209 months (average: 64 months; median 46.5 months). There were a total of 121 implantations (96 primary implantations, 24 revision implantations). There were 2 conversions to the traditional microscopic technique. The exoscope provides a 3D high-definition (4K) images in CI surgery. There is limited data regarding its use, and we identified a number of advantages including efficiency, occupational health, theater utilization, surgical training, and safety. Although there are areas still for improvement, such as loss of signal-to-noise ratio at higher magnification, manual focus, and lack of electromagnetic articulation in the holding arm, we have found it to be a useful addition to the surgical armamentarium within pediatric cochlear implantation.

Interest HD: An Interest Frame Model for Recommendation Based on HD Image Generation.

This work is inspired by high-definition (HD) image generation techniques. When the user's interests are viewed as different frames of varying clarity, the unclear parts of one interest frame can be clarified by other interest frames. The user's overall HD interest portrait can be viewed as a fusion of multiple interest frames through detail compensation. Based on this inspiration, we propose a model for generating HD interest portrait called interest frame for recommendation (IF4Rec). First, we present a fine-grained pixel-level user interest mining method, Pixel embedding (PE) uses positional coding techniques to mine atomic-level interest pixel matrices in multiple dimensions, such as time, space, and frequency. Then, using an atomic-level interest pixel matrix, we propose Item2Frame to generate several interest frames for a user. The similarity score of each item is calculated to fill the multi-interest pixel clusters, through an improved self-attention mechanism. Finally, stimulated by HD image generation techniques, we initially present an interest frame noise compensation method. By utilizing the multihead attention mechanism, pixel-level optimization and noise complementation are performed between multi-interest frames, and an HD interest portrait is achieved. Experiments show that our model mines users' interests well. On five publicly available datasets, our model outperforms the baselines.

Adoption of the Robotic Platform across Thoracic Surgeries.

With the paradigm shift in minimally invasive surgery from the video-assisted thoracoscopic platform to the robotic platform, thoracic surgeons are applying the new technology through various commonly practiced thoracic surgeries, striving to improve patient outcomes and reduce morbidity and mortality. This review will discuss the updates in lung resections, lung transplantation, mediastinal surgeries with a focus on thymic resection, rib resection, tracheal resection, tracheobronchoplasty, diaphragm plication, esophagectomy, and paraesophageal hernia repair. The transition from open surgery to video-assisted thoracoscopic surgery (VATS) to now robotic video-assisted thoracic surgery (RVATS) allows complex surgeries to be completed through smaller and smaller incisions with better visualization through high-definition images and finer mobilization, accomplishing what might be unresectable before, permitting shorter hospital stay, minimizing healing time, and encompassing broader surgical candidacy. Moreover, better patient outcomes are not only achieved through what the lead surgeon could carry out during surgeries but also through the training of the next generation via accessible live video feedback and recordings. Though larger volume randomized controlled studies are pending to compare the outcomes of VATS to RVATS surgeries, published studies show non-inferiority data from RVATS performances. With progressive enhancement, such as overcoming the lack of haptic feedback, and future incorporation of artificial intelligence (AI), the robotic platform will likely be a cost-effective route once surgeons overcome the initial learning curve.

Creating realistic anterior segment optical coherence tomography images using generative adversarial networks

AimsTo develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images.MethodsThis study included 142 628 AS-OCT B-scans from the American University of...

Analyzing strain localization of Chang'E-5 lunar regolith through discrete element analysis

The current understanding of the geotechnical behavior of lunar in-situ resources, particularly lunar regolith (LR), is significantly limited due to its scarcity. To address this gap, this research utilized the morphological characteristics of LR particles obtained from the Chang'E-5 (CE-5) mission to construct numerical simulants using the discrete element method (DEM). This approach was then employed to investigate the mechanical properties of LR. Firstly, high-definition lunar particle images from the CE-5 mission were selected to capture the morphological characteristics and grain size distribution. These morphological characteristics were linked with the rolling resistance parameter and incorporated into the three-dimensional (3D) micromechanical contact model. Additionally, a flexible boundary condition was employed in the triaxial simulation to ensure the evolution of strain localization. The relative particle translation gradient (RPTG) concept was utilized to capture the onset and development of strain localization during the shear process. The results indicated that the numerical lunar simulants can effectively reproduce the mechanical response of LR. Furthermore, at the particle scale, particle shape characteristics play a crucial role in particle rotation and translation during the shear process. This study may establish a foundation for lunar resource exploration and utilization techniques.

DESCRIPTION OF A NEW SPECIES OF THE GENUS DOLIOPS WATERHOUSE, 1841 (COLEOPTERA: CERAMBYCIDAE, LAMIINAE) FROM DAVAO DE ORO WITH NOTES ON OTHER ENDEMIC SPECIES AND MIMICRY COMPLEX IN MINDANAO ISLAND, THE PHILIPPINES

Doliops comvalensis Medina, sp. n. is described from Candalaga Range in Maragusan, Davao de Oro, Mindanao, Philippines. High-definition images of other known species of Doliops which are considered as the close congeners of the new species are also provided. The mimicry complex between the new species and the members of the tribe Pachyrhynchini known from Candalaga Range is discussed.

Application Value of the Apolipoprotein in the Prediction of Acute Ischemic Death and in the Assessment of Functional Outcomes.

In recent years, research on the apolipoprotein E (APOE) gene has gradually proven that many diseases, including atherosclerosis, coronary heart disease, and neurological diseases, are closely related to ApoE gene diversity. However, the relationship between the APOE gene and the prediction and prognosis evaluation of ischemic stroke has not been determined or unified so far. The purpose of this study was to investigate the application value of APOE allele-4 combined with high-resolution vascular wall imaging in predicting the occurrence and prognosis of acute ischemic stroke. A total of 511 patients with acute ischemic stroke (AIS), who were admitted from January 2022 to December 2023, were included in the study, including 317 patients with intracranial artery stenosis. Blood lipids, lipoproteins, apolipoprotein E (including allelic typing), and lipoproteins (a) were measured in all cases, and high-resolution magnetic resonance imaging of the vascular walls was performed. At 6 months, the functional outcomes of the AIS patients were followed up, assessed by using the modified Rankin Scale (mRS) (a score of 2 - 6 was rated as poor prognosis), and the high-definition vascular wall imaging results were followed up as well. High-definition vascular wall imaging ensures the accurate location of vascular stenosis and the accurate diagnosis of acute stroke. There were no significant differences in the total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, or lipoprotein (a) in patients with and without intracranial artery stenosis, but the plasma apolipoprotein E (APOE) levels were significantly reduced in patients with intracranial artery stenosis (ICAS). At the 6-month follow-up, 230 patients with the APOE-ε4 gene were enrolled, out of which 104 had a poor prognosis (mRS score ≥ 2), accounting for 45.22%. Among 281 patients without the APOE-ε4 gene, 45 had a poor prognosis (mRS score ≥ 2), accounting for 16.01%. Patients with the APOE-ε4 gene had a worse functional prognosis after 6 months. It is suggested that low plasma APOE levels may be a high risk factor for ICAS in patients with acute ischemic stroke, and carrying the APOE-ε4 gene may be a high risk factor for a poor functional prognosis in AIS patients. The APOE-ε4 genotype, combined with high-resolution vascular wall imaging, has certain clinical application value in predicting the occurrence of acute ischemic death and evaluating the functional outcome.

A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection.

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors, and complexity of character interactions. Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy. To address the dataset complexity and multi-targeting problems, feature extraction is optimized, and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of the YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students-raising their hands, reading, writing, playing on their cell phones, looking down, and leaning on the table-in high-definition images. And the accuracy and robustness of this network is verified. Compared with small-object detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network demonstrates good detection performance on low-resolution small objects, complex datasets with numerous targets, occlusion, and overlapping students.

18F-FDG-PET/MR imaging to monitor disease activity in large vessel vasculitis

Disease-monitoring in large vessel vasculitis (LVV) is challenging. Simultaneous 18F-fluorodeoxyglucose positron emission tomography with magnetic resonance imaging (PET/MRI) provides functional assessment of vascular inflammation alongside high-definition structural imaging with a relatively low burden of radiation exposure. Here, we investigate the ability of PET/MRI to monitor LVV disease activity longitudinally in a prospective cohort of patients with active LVV. We demonstrate that both the PET and MRI components of the scan can distinguish active from inactive disease using established quantification methods. Using logistic-regression modelling of PET/MRI metrics, we devise a novel PET/MRI-specific Vasculitis Activity using MR PET (VAMP) score which is able to distinguish active from inactive disease with more accuracy than established methods and detects changes in disease activity longitudinally. These findings are evaluated in an independent validation cohort. Finally, PET/MRI improves clinicians’ assessment of LVV disease activity and confidence in disease management, as assessed via clinician survey. In summary, PET/MRI may be useful in tracking disease activity and assessing treatment-response in LVV. Based on our findings, larger, prospective studies assessing PET/MRI in LVV are now warranted.

A two-stage road sign detection and text recognition system based on YOLOv7

We developed a two-stage traffic sign recognition system to enhance safety and prevent tragic traffic incidents involving self-driving cars. In the first stage, YOLOv7 was employed as the detection model for identifying 31 types of traffic signs. Input images were set to 640 × 640 pixels to balance speed and accuracy, with high-definition images split into overlapping sub-images of the same size for training. The YOLOv7 model achieved a training accuracy of 99.2 % and demonstrated robustness across various scenes, earning a testing accuracy of 99 % in both YouTube and self-recorded driving videos. In the second stage, extracted road sign images underwent rectification before processing with OCR tools such as EasyOCR and PaddleOCR. Post-processing steps addressed potential confusion, particularly with city/town names. After extensive testing, the system achieved recognition rates of 97.5 % for alphabets and 99.4 % for Chinese characters. This system significantly enhances the ability of self-driving cars to detect and interpret traffic signs, thereby contributing to safer road travel.

Intelligent skin-removal photoacoustic computed tomography for human based on deep learning.

Photoacoustic computed tomography (PACT) has centimeter-level imaging ability and can be used to detect the human body. However, strong photoacoustic signals from skin cover deep tissue information, hindering the frontal display and analysis of photoacoustic images of deep regions of interest. Therefore, we propose a 2.5 D deep learning model based on feature pyramid structure and single-type skin annotation to extract the skin region, and design a mask generation algorithm to remove skin automatically. PACT imaging experiments on the human periphery blood vessel verified the correctness our proposed skin-removal method. Compared with previous studies, our method exhibits high robustness to the uneven illumination, irregular skin boundary, and reconstruction artifacts in the images, and the reconstruction errors of PACT images decreased by 20% ~ 90% with a 1.65 dB improvement in the signal-to-noise ratio at the same time. This study may provide a promising way for high-definition PACT imaging of deep tissues.

High-Definition Garden Plant Images Threshold Segmentation Mechanism Based on PSO and DRL

The accuracy of the threshold determines the quality of high-definition garden plant image segmentation. How to accurately and quickly search for the best combination of multiple thresholds is currently a research difficulty. In this regard, this article proposes an improved adaptive particle swarm optimization algorithm with extremal disturbance (IAPSO), which can to some extent prevent the PSO from falling into local optima by implementing extreme perturbation strategies. Then, by combining IAPSO and Deep Reinforcement Learning (DRL), the IAPSO-RL based on policy gradient off policy is proposed. It enhances information exchange between DRL and PSO. The IAPSO-RL can improve the sample efficiency of PSO. Experiments have shown that it can improve the performance and stability of threshold segmentation for high-definition garden plant images.

Comparison of Measurement and Prognostic Power of SUV Between High-Definition and Standard PET Imaging in Non-Small Cell Lung Cancer Patients.

This study aimed to evaluate the measurement and prognostic ability of the SUVmax of whole-body tumors (SUVmaxwb) in non-small cell lung cancer (NSCLC) patients, comparing high-definition (HD) PET imaging with standard-definition (SD) PET imaging. Methods: The study included 242 consecutive NSCLC patients who underwent baseline 18F-FDG PET/CT from April 2018 to January 2021. Two imaging techniques were used: HD PET (using ordered-subsets expectation maximization with point-spread function modeling and time-of-flight techniques and smaller voxels) and SD PET (with ordered-subsets expectation maximization and time-of-flight techniques). SUVmaxwb was determined by measuring all the tumor lesions in the whole body, and tumor-to-background ratio (TBR) was calculated using the background SUVmean of various body parts. Results: The patient cohort had an average age of 68.3 y, with 59.1% being female. During a median follow-up of 29.6 mo, 83 deaths occurred. SUVmaxwb was significantly higher in HD PET than SD PET, with respective medians of 17.4 and 11.8. The TBR of 1,125 tumoral lesions was also higher in HD PET. Univariate Cox regression analysis showed that SUVmaxwb from both HD and SD PET were significantly associated with overall survival. However, after adjusting for TNM (tumor, node, metastasis) stage, only SUVmaxwb from SD PET remained significantly associated with survival. Conclusion: HD PET imaging in NSCLC patients yields higher SUVmaxwb and TBR, enhancing tumor visibility. Despite this, its prognostic value is less significant than SD PET after adjusting clinical TNM stage. Thus, consideration should be given to using HD PET reconstruction to increase tumor visibility, and SD PET is recommended for NSCLC patient prognostication and therapeutic evaluation, as well as for the classification of lung nodules.

Reliability of Kudo’s Glandular Pit Pattern in Predicting Colorectal Lesion Histology at Routine Colonoscopy with Digital Chromoendoscopy

Background. The large number of lesions detected via high-definition (HD) imaging during colonoscopy calls for the reliable real-time histological characterization of polyps, especially diminutive and small ones, to permit tailored management based on the neoplastic risk, such as a “resect-and-discard” or a “diagnose-and-leave” strategy for low-risk adenomas and hyperplastic polyps (HPs). The Kudo classification of glandular pit pattern is currently used for predicting polyp histology. Aim. The aim in this study was to assess whether Kudo’s glandular pit pattern, assessed via HD digital chromoendoscopy (i-Scan) without magnification and optical enhancement, reliably predicts polyp histology and differentiates neoplastic lesions (NLs) from non-neoplastic lesions (non-NLs) during routine colonoscopy. Methods. Consecutive colorectal lesions recorded in a database over 12 months, with Kudo’s glandular pit pattern classification, were retrospectively compared with histology. The diagnostic accuracy and negative predictive value (NPV) for adenomatous histology of Kudo’s pit patterns were assessed separately for diminutive (≤5 mm) and small (6–9 mm) polyps, accordingly to the American Society for Gastrointestinal Endoscopy (ASGE) Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI), and in large (≥10 mm) lesions. Results. A total of 2230 lesions were recorded: 898 diminutive, 704 small, and 628 large. Kudo’s type II pit pattern was prevalent in diminutive polyps and recognized mostly in HPs (83.27%); it was also found in 38.8% of adenomas. In the right colon, Kudo’s type II pit pattern was prevalent in adenomas (70.04% vs. 20.74% in HPs); among the serrated lesions, it was evenly distributed between HPs and adenomas. Kudo’s type IIIL/IIIs/IV pit pattern was prevalent in NLs (61% vs. 8.37% of non-NLs) in diminutive polyps, evenly distributed between non-NLs and NLs in small polyps, and found only in NLs in large polyps. Kudo’s type Vi/Vn pit pattern correctly identified all but one adenocarcinoma. The NPV for adenomatous histology did not reach the recommended 90% PIVI threshold for differentiation between NLs and non-NLs in diminutive polyps showing Kudo’s type II pit pattern and in small polyps showing type IIIL/IIIs/IV pit pattern. Conclusions. Kudo’s pit pattern classification carried out with digital chromoendoscopy (i-Scan) during routine colonoscopy does not allow the reliable differentiation between non-NLs and NLs in diminutive and small polyps, so a “diagnose-and-leave” strategy for diminutive polyps may leave undetected adenomas, while a “resect-and-discard” strategy could miss lesions requiring closer follow-up.

3D Multiparametric Photoacoustic Computed Tomography of Primary and Metastatic Tumors in Living Mice.

Photoacoustic computed tomography (PACT), an emerging imaging modality in preclinical cancer research, can provide multiparametric 3D information about structures, physiological functions, and pharmacokinetics. Here, we demonstrate the use of high-definition 3D multiparametric PACT imaging of both primary and metastatic tumors in living mice to noninvasively monitor angiogenesis, carcinogenesis, hypoxia, and pharmacokinetics. The high-definition PACT system with a 1024-element hemispherical ultrasound transducer array provides an isotropic spatial resolution of 380 μm, an effective volumetric field-of-view of 12.8 mm × 12.8 mm × 12.8 mm without scanning, and an acquisition time of <30 s for a whole mouse body. Initially, we monitor the structural progression of the tumor microenvironment (e.g., angiogenesis and vessel tortuosity) after tumor cell inoculation. Then, we analyze the change in oxygen saturation of the tumor during carcinogenesis, verifying induced hypoxia in the tumor's core region. Finally, the whole-body pharmacokinetics are photoacoustically imaged after intravenous injection of micelle-loaded IR780 dye, and the in vivo PACT results are validated in vivo and ex vivo by fluorescence imaging. By employing the premium PACT system and applying multiparametric analyses to subcutaneous primary tumors and metastatic liver tumors, we demonstrate that this PACT system can provide multiparametric analyses for comprehensive cancer research.

Computational image analysis of distortion, sharpness, and depth of field in a next-generation hybrid exoscopic and microsurgical operative platform.

The development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures. We built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately. A total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode. The evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.