Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Recent advancements in image-enhanced endoscopy in the pancreatobiliary field.

Image-enhanced endoscopy (IEE) has advanced gastrointestinal disease diagnosis and treatment. Traditional white-light imaging has limitations in detecting all gastrointestinal diseases, prompting the development of IEE. In this review, we explore the utility of IEE, including texture and color enhancement imaging and red dichromatic imaging, in pancreatobiliary (PB) diseases. IEE includes methods such as chromoendoscopy, optical-digital, and digital methods. Chromoendoscopy, using dyes such as indigo carmine, aids in delineating lesions and structures, including pancreato-/cholangio-jejunal anastomoses. Optical-digital methods such as narrow-band imaging enhance mucosal details and vessel patterns, aiding in ampullary tumor evaluation and peroral cholangioscopy. Moreover, red dichromatic imaging with its specific color allocation, improves the visibility of thick blood vessels in deeper tissues and enhances bleeding points with different colors and see-through effects, proving beneficial in managing bleeding complications post-endoscopic sphincterotomy. Color enhancement imaging, a novel digital method, enhances tissue texture, brightness, and color, improving visualization of PB structures, such as PB orifices, anastomotic sites, ampullary tumors, and intraductal PB lesions. Advancements in IEE hold substantial potential in improving the accuracy of PB disease diagnosis and treatment. These innovative techniques offer advantages paving the way for enhanced clinical management of PB diseases. Further research is warranted to establish their standard clinical utility and explore new frontiers in PB disease management.

Scientists in the news photos: Photographic portraits of scientists in China (1949-2022).

Photography plays an important role in science communication. This study investigates the photographic portraits of scientists in the news media in China from 1949 to 2022. The data consist of 1,071 photographs published in People's Daily, the most influential newspaper in China. The photographs are analysed according to a framework based on previous studies on the visual representation of scientists. Analysis shows an overall image of scientists that demonstrates distinctive 'Chinese' features, such as the prominence of group photos and governmental honours. Diachronically, the visual image of scientists evolved from the early farmer scientists acclaimed in midst of political struggle to social elites and stars celebrated as China's hope for indigenous innovation. The study enriches our understanding of the visual representation of scientists in China, and sheds light on the influence of culture, politics and social positioning of science and technology on the image of scientists created by the media.

Harnessing 3D Printing and Electrospinning for Multiscale Hybrid Patches Mimicking the Native Myocardium.

Engineered cardiac tissues show potential for regenerative therapy in ischemic heart disease. Yet, selection of soft biomaterials for scaffold manufacturing is primarily influenced by empirical and compositional factors, raising concerns about arrhythmic risks due to poor electrophysiological integration. Addressing this, we developed multiscale hybrid myocardial patches mimicking native myocardium's structural and biomechanical attributes, utilizing 3D printing and electrospinning techniques. We compared three patch types: pure silicone and silicone-poly(lactic-co-glycolic acid) (PLGA) with random (S-PLGA-R) and aligned (S-PLGA-A) fibers. S-PLGA-A patches with fiber orientation angles of 95-115° are achieved by applying a secondary electrical field using two parallel aluminum enhancers. With bulk and localized moduli of 350-750 and 13-20 kPa resembling the native myocardium, S-PLGA-A patches demonstrate a sarcomere length of 2.1 ± 0.2 µm, ≥50% higher strain motions and diastolic phase, and a 50-70% slower rise of calcium handling compared to the other two patches. This enhanced maturation and improved synchronization phenomena are attributed to efficient force transmission and reduced stress concentration due to mechanical similarity and linear propagation of electrical signals. This study presents a promising strategy for advancing regenerative cardiac therapies by harnessing the capabilities of 3D printing and electrospinning, providing a proof-of-concept for their effectiveness.

Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population.

BACKGROUND: Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. METHODS: We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. RESULTS: Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. CONCLUSION: These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

The artistic image processing for visual healing in smart city.

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents' mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants.

An animal's locomotor rate is an important indicator of its motility. In studies of the nematode C. elegans, assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e., shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hu's moment invariants and a set of Maximally Stable Extremal Regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to a segmentation-based method that analyzes a worm's shape and a method based on video covariance, our technique is more robust to low image quality and background noise. We demonstrate the system's capabilities by testing the effects of serotonin and serotonin pathway mutations on C. elegans locomotor frequency.

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam.

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models-VGG16, ResNet50, and InceptionV3-combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients.

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (HC). These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN) and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to healthy control group. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. k-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.

Research on detection method of photovoltaic cell surface dirt based on image processing technology.

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Integration of autonomous maximal smile 3D image with digital 3D dental model and investigation of its accuracy.

OBJECTIVES: This study aims to establish an approach to integrate autonomous maximal smile (AMS) 3D facial image with digital 3D dental models to demonstrate the digital orthodontic set-up in the 3D facial context. METHODS: Using Geomagic Studio software, the AMS 3D facial image and pre-treatment dental model were manually and globally registered. Subsequently, the pre-treatment dental model was substituted with the predicted post-treatment dental model. The intraoral region of the AMS 3D facial image was removed, achieving a conjunctive display of the AMS 3D facial image and the post-treatment dental set-up. The distances between four groups of corresponding landmark pairs on the AMS 3D facial image and the pre-treatment dental set-up were calculated, and the accuracy of the registration operation was evaluated by paired t-test. RESULTS: The novel approach effectively facilitated the integration of AMS 3D facial images with the pre-treatment and predicted post-treatment 3D dental models. The average distances between the pairs of points were (1.19±0.55) mm and (1.55±0.59) mm for the two registrations, respectively. Notably, no statistically significant difference was observed between the two measurements (P>0.05), indicating a high agreement (intraclass correlation coefficient=0.914). CONCLUSIONS: This study established an approach to integrate AMS 3D facial images with digital 3D dental models. Through this approach, the digital orthodontic set-up design can be displayed in the context of a 3D facial image, which may help to improve the quality of outcome set-up in digital orthodontics, such as clear aligner therapy.

[Trans-YOLOv5: a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images].

The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis. Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology, but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself, but also involves the comparison with the surrounding cells. Herein we present the Trans-YOLOv5 model, an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images. The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods, with a mAP reaching 65.9% and an AR reaching 53.3%, showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.

Large Foundation Model for Cancer Segmentation.

Recently, large language models such as ChatGPT have made huge strides in understanding and generating human-like text and have demonstrated considerable success in natural language processing. These foundation models also perform well in computer vision. However, there is a growing need to use these technologies for specific medical tasks, especially for identifying cancer in images. This paper looks at how these foundation models, such as the segment anything model, could be used for cancer segmentation, discussing the potential benefits and challenges of applying large foundation models to help with cancer diagnoses.

Multi-modal co-learning with attention mechanism for head and neck tumor segmentation on 18FDG PET-CT.

PURPOSE: Effective radiation therapy requires accurate segmentation of head and neck cancer, one of the most common types of cancer. With the advancement of deep learning, people have come up with various methods that use positron emission tomography-computed tomography to get complementary information. However, these approaches are computationally expensive because of the separation of feature extraction and fusion functions and do not make use of the high sensitivity of PET. We propose a new deep learning-based approach to alleviate these challenges. METHODS: We proposed a tumor region attention module that fully exploits the high sensitivity of PET and designed a network that learns the correlation between the PET and CT features using squeeze-and-excitation normalization (SE Norm) without separating the feature extraction and fusion functions. In addition, we introduce multi-scale context fusion, which exploits contextual information from different scales. RESULTS: The HECKTOR challenge 2021 dataset was used for training and testing. The proposed model outperformed the state-of-the-art models for medical image segmentation; in particular, the dice similarity coefficient increased by 8.78% compared to U-net. CONCLUSION: The proposed network segmented the complex shape of the tumor better than the state-of-the-art medical image segmentation methods, accurately distinguishing between tumor and non-tumor regions.

Virtual Loupes: A Pilot Study on the Use of Video Passthrough Augmented Reality in Plastic Surgery.

BACKGROUND: Plastic surgeons use loupes or operative microscope to aid in tissue dissection and anastomosis of structures. These devices have their own limitations in areas of visualization and weight. Current uses of augmented and virtual reality in surgery have been limited to operative planning and simulation. We present a proof of concept that harnesses video passthrough AR technology to augment the capabilities of loupes. METHODS: We first evaluated methods of gaze-based eye tracking to enable digital magnification. Using the Varjo XR-1 mixed reality headset, we compared discrete zoom through displayed pop-up menu vs continuous zoom through eye winking. Six participants were recruited to perform skin suturing simulation and completed a survey and interview. Next we assessed the performance and limitations of AR digital magnification. Varjo XR-3 was utilized to address the hardware limitations. Participants performed anastomotic suturing tasks with progressively finer suture, then completed a survey and interview. FINDINGS: There was no strong preference between zoom methods, although participants felt the discrete zoom was easier to use. Participants had difficulty determining depth and visualizing the suture due to limitations of digital magnification. Using Wilcoxon rank sum test to examine differences in system usability scale, the Phase 2 user experience had significant difference in percentile distribution (P 0.0390). CONCLUSION: Virtual loupes may be a valuable tool for plastic surgeons, with potential for variable magnification and advanced visualization. Improvements in the hardware yielded higher ratings of system usability and user experience. Further development is needed to address the limitations of existing devices.

Automatic segmentation of knee CT images of tibial plateau fractures based on three-dimensional U-Net: Assisting junior physicians with Schatzker classification.

PURPOSE: This study aimed to automatically segment knee computed tomography (CT) images of tibial plateau fractures using a three-dimensional (3D) U-net-based method, accurately construct 3D maps of tibial plateau fractures, and examine their usefulness for Schatzker classification in clinical practice. METHODS: We retrospectively enrolled 234 cases with tibial plateau fractures from our hospital in this study. The four constituent bones of the knee were manually annotated using ITK-SNAP software. Finally, image features were extracted using deep learning. The usefulness of the results for Schatzker classification was examined by an orthopaedic and a radiology resident. RESULTS: On average, our model required < 40 s to process a 3D CT scan of the knee. The average Dice coefficient for all four knee bones was higher than 0.950, and highly accurate 3D maps of the tibia were produced. With the aid of the results of our model, the accuracy, sensitivity, and specificity of the Schatzker classification of both residents improved. CONCLUSIONS: The proposed method can rapidly and accurately segment knee CT images of tibial plateau fractures and assist residents with Schatzker classification, which can help improve diagnostic efficiency and reduce the workload of junior doctors in clinical practice.

Polyenergetic reconstruction mitigates streak artifacts by dual source imaging in chest photon counting detector computed tomography.

OBJECTIVE: This study aims to assess the efficacy of polyenergetic reconstruction methods in reducing streak artifacts caused by dual source imaging in Photon Counting Detector Computed Tomography (PCD-CT) imaging, thereby improving image quality and diagnostic accuracy. METHODS: A retrospective cohort study was conducted, involving 50 patients who underwent chest Computed Tomography Angiography with PCD-CT, focusing on those with streak artifacts. Quantitative and qualitative analyses were performed on images reconstructed using monoenergetic and polyenergetic techniques. Quantitative evaluations measured the attenuation of tracheal air density in regions affected by streak artifacts, while qualitative assessments employed a modified Likert scale to rate image quality. Statistical analyses included Wilcoxon's signed-rank tests and Spearman's correlation, alongside assessments of inter-rater reliability. RESULTS: There was significantly lower attenuation of tracheal air density on the polyenergetic reconstructions (Median - 1010 ± 62 HU vs -930 ± 110 HU; P < 0.001), and significantly decreased variation on the polyenergetic reconstructions (Median 65.2 ± 79.5 HU vs 38.8 ± 33.9 HU; P < 0.001). The median modified-Likert scale were significantly better for the polyenergetic reconstructions (median modified-Likert 4 ± 0.5 vs 2.5 ± 1; P < 0.001). The inter-rater agreement was substantial and not significantly different between reconstructions (Gwet's ACPolyenergetic = 0.78 vs Gwet's ACVMI = 0.775). CONCLUSION: Polyenergetic reconstruction significantly mitigates streak artifacts in PCD-CT imaging, enhancing quantitative and qualitative image quality. This advancement addresses a known limitation of current PCD-CT reconstruction techniques, offering a promising approach to improving diagnostic reliability and accuracy in clinical practice. We demonstrate that future software implementations can resolve this artifact.

Effects of online mindful self-compassion intervention on negative body image in breast cancer patients: A randomized controlled trail.

PURPOSE: The incidence of breast cancer patients with negative body image has increased. However, research on interventions that explicitly reduce negative body image among breast cancer patients remains inadequate. The development of more pragmatic interventions is imperative. Therefore, we conducted this study to assess the effectiveness of a 6-week online Mindful Self-Compassion (MSC) intervention to reduce the negative body image in breast cancer patients. METHODS: We randomly assigned 64 female breast cancer patients to either the MSC group or the control group. The MSC group received a 6-week online Mindful Self-Compassion intervention, while the control group received no psychological training. Participants were surveyed by the Self-Compassion Scale-Short Form (SCS-SF), the Self-Acceptance Questionnaire (SAQ), the Chinese Perceived Stress Scale (CPSS), and the Body Image Scale (BIS) at baseline (T1), post-intervention (T2), and 1-month follow-up (T3). RESULTS: All outcome variables demonstrated significant time main effects and nonsignificant group main effects. The MSC and control groups had significant time × group interaction effects on self-compassion, self-acceptance, perceived stress, and negative body image. Simple main effects analysis revealed significant improvements in outcome variables at three-time points for the MSC group. CONCLUSION: A 6-week online Mindful Self-Compassion intervention can improve self-compassion and self-acceptance and reduce perceived stress and negative body image among the breast cancer patients in MSC group. Mindful Self-Compassion intervention shows promise as a viable way to maintain the mental well-being of breast cancer patients.

Federated brain tumor segmentation: An extensive benchmark.

Recently, federated learning has raised increasing interest in the medical image analysis field due to its ability to aggregate multi-center data with privacy-preserving properties. A large amount of federated training schemes have been published, which we categorize into global (one final model), personalized (one model per institution) or hybrid (one model per cluster of institutions) methods. However, their applicability on the recently published Federated Brain Tumor Segmentation 2022 dataset has not been explored yet. We propose an extensive benchmark of federated learning algorithms from all three classes on this task. While standard FedAvg already performs very well, we show that some methods from each category can bring a slight performance improvement and potentially limit the final model(s) bias toward the predominant data distribution of the federation. Moreover, we provide a deeper understanding of the behavior of federated learning on this task through alternative ways of distributing the pooled dataset among institutions, namely an Independent and Identical Distributed (IID) setup, and a limited data setup. Our code is available at (https://github.com/MatthisManthe/Benchmark_FeTS2022).