Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

BACKGROUND AND OBJECTIVE: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm. METHODS: We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test. RESULTS: Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33). CONCLUSION: Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .

Key anti-freeze genes and pathways of Lanzhou lily (Lilium davidii, var. unicolor) during the seedling stage.

Temperature is one of the most important environmental factors for plant growth, as low-temperature freezing damage seriously affects the yield and distribution of plants. The Lanzhou lily (Lilium davidii, var. unicolor) is a famous ornamental plant with high ornamental value. Using an Illumina HiSeq transcriptome sequencing platform, sequencing was conducted on Lanzhou lilies exposed to two different temperature conditions: a normal temperature treatment at 20°C (A) and a cold treatment at -4°C (C). After being treated for 24 hours, a total of 5848 differentially expressed genes (DEGs) were identified, including 3478 significantly up regulated genes and 2370 significantly down regulated genes, accounting for 10.27% of the total number of DEGs. Quantitative real-time PCR (QRT-PCR) analysis showed that the expression trends of 10 randomly selected DEGs coincided with the results of high-throughput sequencing. In addition, genes responding to low-temperature stress were analyzed using the interaction regulatory network method. The anti-freeze pathway of Lanzhou lily was found to involve the photosynthetic and metabolic pathways, and the key freezing resistance genes were the OLEO3 gene, 9 CBF family genes, and C2H2 transcription factor c117817_g1 (ZFP). This lays the foundation for revealing the underlying mechanism of the molecular anti-freeze mechanism in Lanzhou lily.

GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy.

With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.

ChatGPT in healthcare: A taxonomy and systematic review.

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc.). However, it is crucial, especially for healthcare researchers, to remain prudent amidst the hype. This work provides a systematic review of existing publications on the use of ChatGPT in healthcare, elucidating the 'status quo' of ChatGPT in medical applications, for general readers, healthcare professionals as well as NLP scientists. The large biomedical literature database PubMed is used to retrieve published works on this topic using the keyword 'ChatGPT'. An inclusion criterion and a taxonomy are further proposed to filter the search results and categorize the selected publications, respectively. It is found through the review that the current release of ChatGPT has achieved only moderate or 'passing' performance in a variety of tests, and is unreliable for actual clinical deployment, since it is not intended for clinical applications by design. We conclude that specialized NLP models trained on (bio)medical datasets still represent the right direction to pursue for critical clinical applications.

3D surgical instrument collection for computer vision and extended reality.

The availability of computational hardware and developments in (medical) machine learning (MML) increases medical mixed realities' (MMR) clinical usability. Medical instruments have played a vital role in surgery for ages. To further accelerate the implementation of MML and MMR, three-dimensional (3D) datasets of instruments should be publicly available. The proposed data collection consists of 103, 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments, like retractors, forceps, and clamps. The collection can be augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings, or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality and medical diminishing reality in mixed reality. We hope to ease research in the field of MMR and MML, but also to motivate the release of a wider variety of needed surgical instrument datasets.

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution.

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes only on the non-empty voxels, thus reducing the memory and computation overhead caused by the sparse input data. We evaluate our method on two clinically relevant skull reconstruction tasks: (1) given a defective skull, reconstruct the complete skull (i.e., skull shape completion), and (2) given a coarse skull, reconstruct a high-resolution skull with fine geometric details (shape super-resolution). Our method outperforms its dense CNN-based counterparts in the skull reconstruction task quantitatively and qualitatively, while requiring substantially less memory for training and inference. We observed that, on the 3D skull data, the overall memory consumption of the sparse CNN grows approximately linearly during inference with respect to the image resolutions. During training, the memory usage remains clearly below increases in image resolution-an [Formula: see text] increase in voxel number leads to less than [Formula: see text] increase in memory requirements. Our study demonstrates the effectiveness of using a sparse CNN for skull reconstruction tasks, and our findings can be applied to other spatially sparse problems. We prove this by additional experimental results on other sparse medical datasets, like the aorta and the heart. Project page at https://github.com/Jianningli/SparseCNN .

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations.

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Observer-based H∞ fuzzy fault-tolerant switching control for ship course tracking with steering machine fault detection.

To enhance the robustness of ship autopilot (SA) system with nonlinear dynamics, unmeasured states, and unknown steering machine fault, an observer-based H∞ fuzzy fault-tolerant switching control for ship course tracking is proposed. Firstly, a global Takagi-Sugeno (T-S) fuzzy nonlinear ship autopilot (NSA) is developed with full consideration of ship steering characteristics. And the actual navigation data collected from a real ship are used to verify the reasonableness and feasibility of NSA model. Then, virtual fuzzy observers (VFOs) for both fault-free and faulty systems are proposed to estimate the unmeasured states and unknown fault simultaneously, and compensate for the faulty system by using the fault estimates. Accordingly, the VFO-based H∞ robust controller (VFO-HRC) and fault-tolerant controller (VFO-HFTC) are designed. Subsequently, a smoothed Z-score-based fault detection and alarm (FDA) is developed to provide switching signals for which the controller and its corresponding observer should be invoked. Finally, simulation results on the "Yulong" ship demonstrate the effectiveness of the developed control method.

The HoloLens in medicine: A systematic review and taxonomy.

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine.

Elevated fasting glucose level increases the risk of fatty liver disease: a 10-year study of 31,154 individuals.

OBJECTIVES: Dysglycemia promotes the occurrence of fatty liver disease (FLD). However, the process is unclear. This study aimed to analyze the median time-to-onset, cumulative prevalence and influencing factors for the occurrence of FLD in people undergoing routine screening and evaluation. METHODS: Data from Karamay Central Hospital (September 2008-April 2017) were analyzed. Survival analysis was performed to calculate the median time and cumulative prevalence of FLD associated with normal and elevated fasting blood glucose (FBG) levels. Cox proportional hazards model was used to determine risk factors. RESULTS: A total of 31,154 participants were included in the two cohorts of this study, including 15,763 men. The mean age was 41.1 ± 12.2 years. There were 2230 patients (1725 male) in the elevated FBG group, the median age was 53 years (range 21-85 years), the median time-to-onset of FLD was 5.2 years. The incidence of FLD was 121/1000 person-years, and the 1-, 3-, 5-, and 7-year prevalence rates were 4%, 30%, 49%, and 64%, respectively. The normal FBG group included 28,924 participants (14,038 male), the median age was 40 years (range 17-87 years), and the corresponding values were as follows: 8.3 years, 66/1000 person-years, and 3%, 16%, 28%, and 41%, respectively. The Cox proportional hazards analysis revealed that age, blood pressure, FBG, body mass index and triglycerides were independent influencing factors for FLD in individuals (P < 0.05). CONCLUSIONS: Elevated FBG levels increase the risk of FLD and should be treated promptly.

Radiomics in Head and Neck Cancer Outcome Predictions.

Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients' clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.

CranGAN: Adversarial Point Cloud Reconstruction for patient-specific Cranial Implant Design.

Automatizing cranial implant design has become an increasingly important avenue in biomedical research. Benefits in terms of financial resources, time and patient safety necessitate the formulation of an efficient and accurate procedure for the same. This paper attempts to provide a new research direction to this problem, through an adversarial deep learning solution. Specifically, in this work, we present CranGAN - a 3D Conditional Generative Adversarial Network designed to reconstruct a 3D representation of a complete skull given its defective counterpart. A novel solution of employing point cloud representations instead of conventional 3D meshes and voxel grids is proposed. We provide both qualitative and quantitative analysis of our experiments with three separate GAN objectives, and compare the utility of two 3D reconstruction loss functions viz. Hausdorff Distance and Chamfer Distance. We hope that our work inspires further research in this direction. Clinical relevance- This paper establishes a new research direction to assist in automated implant design for cranioplasty.

Redox-Unlockable Nanoparticle-Based MST1 Delivery System to Attenuate Hepatic Steatosis via the AMPK/SREBP-1c Signaling Axis.

To date, few effective treatments have been licensed for nonalcoholic fatty liver disease (NAFLD), which a kind of chronic liver disease. Mammalian sterile 20-like kinase 1 (MST1) is reported to be involved in the development of NAFLD. Thus, we evaluated the suitability of a redox-unlockable polymeric nanoparticle Hep@PGEA vector to deliver MST1 or siMST1 (HCP/MST1 or HCP/siMST1) for NAFLD therapy. The Hep@PGEA vector can efficiently deliver the condensed functional nucleic acids MST1 or siMST1 into NAFLD-affected mouse liver to upregulate or downregulate MST1 expression. The HCP/MST1 complexes significantly improved liver insulin resistance sensitivity and reduced liver damage and lipid accumulation by the AMPK/SREBP-1c pathway without significant adverse events. Instead, HCP/siMST1 delivery exacerbates the NAFLD. The analysis of NAFLD patient samples further clarified the role of MST1 in the development of hepatic steatosis in patients with NAFLD. The MST1-based gene intervention is of considerable potential for clinical NAFLD therapy, and the Hep@PGEA vector provides a promising option for NAFLD gene therapy.

Medical deep learning-A systematic meta-review.

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research efforts. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys.

Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform.

Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster ( www.studierfenster.at ): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448-456, 68], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology.

Circular RNA circ-LARP1B contributes to cutaneous squamous cell carcinoma progression by targeting microRNA-515-5p/TPX2 microtubule nucleation factor axis.

Circular RNAs (circRNAs) have shown pivotal regulatory roles in tumorigenesis and progression. Our purpose was to analyze the role of circRNA La ribonucleoprotein 1B (circ-LARP1B; hsa_circ_0070934) in cutaneous squamous cell carcinoma (CSCC) progression and its associated mechanism. Cell viability, colony formation ability, migration, and invasion were analyzed by 3-(4, 5-dimethylthiazol-2-yl)-2, 5 diphenyltetrazolium bromide (MTT) assay, colony formation assay, wound healing assay, and transwell invasion assay. Flow cytometry was performed to analyze cell apoptosis and cell cycle progression. Cell glycolytic metabolism was analyzed using Glucose Uptake Colorimetric Assay kit, Lactate Assay Kit II, and ATP colorimetric Assay kit. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were performed to verify the interaction between microRNA-515-5p (miR-515-5p) and circ-LARP1B or TPX2 microtubule nucleation factor (TPX2). Circ-LARP1B expression was up-regulated in CSCC tissues and cell lines. Circ-LARP1B knockdown suppressed cell viability, colony formation ability, migration, invasion, cell cycle progression, and glycolysis and triggered cell apoptosis in CSCC cells. miR-515-5p was a direct target of circ-LARP1B in CSCC cells, and circ-LARP1B silencing-mediated anti-tumor effects were largely counteracted by miR-515-5p knockdown. miR-515-5p directly interacted with the 3' untranslated region (3'UTR) of TPX2. TPX2 overexpression largely overturned miR-515-5p-mediated anti-tumor effects in CSCC cells. Circ-LARP1B could up-regulate TPX2 expression by sponging miR-515-5p in CSCC cells. Circ-LARP1B knockdown suppressed tumor growth in vivo. In conclusion, circ-LARP1B contributed to CSCC progression by targeting miR-515-5p/TPX2 axis. The circ-LARP1B/miR-515-5p/TPX2 axis might provide novel therapeutic targets for CSCC patients.

AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks.

In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.