Enhanced Image Analysis Research Articles

Ensemble-based deep learning model for welding defect detection and classification

This study introduces an ensemble-based deep learning approach for monitoring and detecting submerged arc weld defects in weld beads and adjacent zones during Non-Destructive Testing (NDT). The methodology utilizes an open-source image dataset to categorize submerged arc weld conditions into three defect types: cracks, lack of penetration and porosity, and ideal welds (no defects). To enhance image analysis, we employ preprocessing and feature extraction in both spatial and frequency domains using segmentation techniques like grey level difference method (GLDM), grey level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), Fast Fourier Transform (FFT), and texture analysis. Following image preparation, the preprocessed data undergoes training and testing in our proposed ensemble-based deep learning model. The model's performance is thoroughly assessed using key metrics such as precision, recall, F1 score, receiver operating characteristics (ROC) curve, and a confusion matrix, resulting in an impressive accuracy rate of 93.12% for detecting and classifying weld faults. In comparing our model to state-of-the-art techniques in existing literature, our ensemble-based deep learning approach consistently demonstrates a high fault detection and classification performance in the face of a simplistic MLP-based core architecture, ratifying the robustness of our feature ensemble approach. The study concludes that this model has significant potential for integration into current inspection systems, offering an efficient and robust solution for real-time condition monitoring of welds along weldment joints.

Remote intelligent perception system for multi-object detection.

During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities. Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image. The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset. Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures

The human body consists of diverse subcellular, cellular and supracellular structures. Neurons possess varying-sized projections that interact with different cellular structures leading to the development of highly complex morphologies. Aiming to enhance image analysis of complex biological forms including neurons using available FIJI (ImageJ) plugins, Lusca, an advanced open-source tool, was developed. Lusca utilizes machine learning for image segmentation with intensity and size thresholds. It performs particle analysis to ascertain parameters such as area/volume, quantity, and intensity, in addition to skeletonization for determining length, branching, and width. Moreover, in conjunction with colocalization measurements, it provides an extensive set of 29 morphometric parameters for both 2D and 3D analysis. This is a significant enhancement compared to other scripts that offer only 5–15 parameters. Consequently, it ensures quicker and more precise quantification by effectively eliminating noise and discerning subtle details. With three times larger execution speed, fewer false positive and negative results, and the capacity to measure various parameters, Lusca surpasses other existing open-source solutions. Its implementation of machine learning-based segmentation facilitates versatile applications for different cell types and biological structures, including mitochondria, fibres, and vessels. Lusca’s automated and precise measurement capability makes it an ideal choice for diverse biological image analyses.

Abstract 914: Combination analysis of tumor-associated collagen frameworks and tumor immune phenotype of lung carcinomas using virtual staining

Abstract An emerging predictive parameter of immunotherapy response is the patient’s tumor immune status, generally classified as “inflamed”, "immune excluded” and “desert”. Historically, classification was performed semi-quantitatively with somewhat subjective parameters as evidenced by the recent Delphi Workshop consensus. There are a variety of approaches to classifying immune status, most based upon analysis of H&E images (counting tumor infiltrating lymphocytes, TILs), or counting subpopulations of immune cells using immunologic staining. However, use of multiplexed or multiple stains has recently been explored to better quantify the counts and types of immune cells in individual tissue compartments. While the immune microenvironment represents an important parameter in predicting patient response, other regions such as the extracellular matrix (ECM) may yield complementary information, as collagen-rich ECMs may present a barrier to drug diffusion and the orientation of fibers may direct the migration of malignant cells. Here we present a proof of concept virtual staining enhanced image analysis pipeline, which converts autofluorescence signals from a single tissue section into virtual H&E and Masson’s Trichrome along with virtual detection of pan cytokeratin (AE1/AE3/PCK26) and CD45 (LCA) positive cells. We apply image analysis to the panel of virtual stains to study the spatial and case-by-case heterogeneity of tumor collagen frameworks and immune phenotype. Using a standard slide scanner (Axio Scan Z1, Zeiss), multiple autofluorescence images were captured from unstained sections (4-6um thick) of lung tissue. The virtual staining was performed by four deep neural networks trained in a supervised learning fashion. Select chemical stains were performed on previously scanned tissues and reviewed by pathologists side-by-side with virtual stains to ensure consistency and quality control. Four perfectly registered WSI virtual stain images were generated from each tissue section. The multi-stain results were rendered from a variety of lung cancers, including tissue microarray slides. Image analysis was performed using HALO (Indica Labs) and custom python scripts. We identified unique areas of tumor and adjacent stroma with heterogeneous immune phenotype and collagen characteristics, suggesting that an interplay might exist that could be utilized to improve patient selection or prognosis when deploying advanced AI tools. Future work includes applying this virtual stain technique retrospectively to cases with known immunotherapy treatment responses to determine the prognostic significance of the combined immune/ECM phenotype. Citation Format: Serge Alexanian, Yair Rivenson, Ning Xuan, Brian Cone, Zihang Fang, Sean Meyering, Luis A. Carvalho, David Palacios, Raymond Kozikowski. Combination analysis of tumor-associated collagen frameworks and tumor immune phenotype of lung carcinomas using virtual staining [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 914.

AI-powered oral cancer detection: A breakthrough in dental diagnostics

Oral cancer is a global issue that poses challenges in both diagnosis and treatment. About 50% of cases are detected at an advanced stage, leading to a low 5-year survival rate. Visual assessments are conventionally employed in dental clinics for the detection of oral cancer, but, their precision is greatly influenced by the proficiency and knowledge of the practitioner, which can significantly differ. Furthermore, the subjective character of these tests might lead to inconsistencies in the diagnosis, hence increasing the probability of either an under- or overdiagnosis. Moreover, the diagnosis of oral cancer entails a substantial emotional burden as a result of protracted and intricate medical interventions, which in turn induces considerable worry in patients. In recent times, artificial intelligence has begun to assist in the diagnosis of oral cancer through many methods. AI-based diagnostic systems have the capability to analyse large amounts of data, including intraoral camera pictures, radiographs, and advanced imaging like MRI or CT scans. This allows them to detect even small changes and enhance image analysis. Thus, the present paper discusses the ways AI powered oral cancer detection can reduce the risks and increase accuracy to give better quality of life to patients.

Artificial intelligence and big data integration in anterior segment imaging for glaucoma.

The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.

Artificial Intelligence in Perioperative Planning and Management of Liver Resection.

Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

The timely identification of pulmonary embolism is of utmost importance, as the condition has the potential to be life-threatening if not promptly addressed. The assessment of the severity of a pulmonary embolism (PE) frequently necessitates a time-consuming and potentially life-threatening estimation by a medical practitioner. The primary objective of the study was to investigate the potential utility of an artificial neural network in assisting physicians with the identification and prediction of pulmonary embolism risk in patients. Deep learning algorithms are frequently employed in medical imaging to enhance image analysis due to their ability to automatically learn representations from large datasets, as opposed to relying on pre-programmed instructions . The implementation of automated systems has the potential to decrease the level of physical effort needed and enhance the efficiency of diagnostic procedures for medical professionals. Efficient training and calculation processes are crucial for the proper execution of the implementation. The Tensor Processing Units (TPUs) developed by Google are employed to expedite the process of training., with the computational tasks being executed through Google Colab, a platform offered by Google Cloud TPUs. In order to achieve outcomes comparable to human judgment, deep learning algorithms engage in reasonable assessments of data based on a predetermined logical framework. Diagnosing pulmonary embolism (PE), a potentially lethal yet curable condition, poses challenges in early detection. A distinctive convolutional neural network (CNN) model was developed and examined for the purpose of distinguishing between pulmonary embolism (PE) and computed tomography (CT) pictures. The proposed study yields a precision rate of 91.2%, showcasing an enhancement compared to current convolutional neural network (CNN) architectures that include limited trainable parameters. Furthermore, our model provides interpretability by the utilization of computed tomography (CT) images, specifically in the inferno and bone models. Our proposed deep learning model has the potential to predict the presence of PE and other associated features in current cases.

Empowering precision medicine: regenerative AI in breast cancer.

Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging system are improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges.

Hyperspectral enhanced imaging analysis of nanoparticles using machine learning methods.

Nanoparticle (NP)-based technologies have gained significant attention in targeted drug delivery, encompassing chemotherapies, photodynamic therapy, and immunotherapy. Hyperspectral imaging (HSI) emerges as a label-free, minimally invasive, and high-throughput technique for quantitative NP analysis. Despite its growing importance, the application of HSI to nanoparticle analysis, especially for label-free characterization and classification, remains limited. Here, we propose a novel method integrating hyperspectral imaging with a spectral noise reduction method and machine learning (ML) for robust nanoparticle classification. There are many challenges to extracting information from noisy and overlapping particles in HSI data. To surmount these challenges, we propose a spectral angle matching (SAM) algorithm to effectively denoise hyperspectral datasets. Complementing this, we employ a support vector machine (SVM) algorithm for classification, leveraging preprocessed HSI data to extract unique spectral signatures. Our hyperspectral imaging classification of multiple nanoparticle types reveals distinct spectral characteristics inherent to each class. The classification accuracy reaches 99.9% for single nanoparticle types, highlighting the efficiency of our method. In the case of classifying multiple particle types, the overall accuracy also reaches 99.9%. Visualization of the NP classification map further demonstrates the efficacy of our model. The application of the SAM-SVM algorithm in hyperspectral analysis outperforms traditional SVM methods in classifying multiple samples, highlighting the potential of our nanoparticle analysis. Our findings not only address the challenges posed by noisy and overlapping particles but also demonstrate the potential of hyperspectral imaging in advancing real-time and label-free detection systems for diverse biomedical applications.

Ultrasound and Nanomedicine for Cancer-Targeted Drug Delivery: Screening, Cellular Mechanisms and Therapeutic Opportunities.

Cancer is a disease characterized by abnormal cell growth. According to a report published by the World Health Organization (WHO), cancer is the second leading cause of death globally, responsible for an estimated 9.6 million deaths in 2018. It should be noted that ultrasound is already widely used as a diagnostic procedure for detecting tumorigenesis. In addition, ultrasound energy can also be utilized effectively for treating cancer. By filling the interior of lipospheres with gas molecules, these particles can serve both as contrast agents for ultrasonic imaging and as delivery systems for drugs such as microbubbles and nanobubbles. Therefore, this review aims to describe the nanoparticle-assisted drug delivery system and how it can enhance image analysis and biomedicine. The formation characteristics of nanoparticles indicate that they will accumulate at the tumor site upon ultrasonic imaging, in accordance with their modification characteristics. As a result of changing the accumulation of materials, it is possible to examine the results by comparing images of other tumor cell lines. It is also possible to investigate ultrasound images for evidence of cellular effects. In combination with a precision ultrasound imaging system, drug-carrying lipospheres can precisely track tumor tissue and deliver drugs to tumor cells to enhance the ability of this nanocomposite to treat cancer.

High-Throughput Microfluidics Platform for Intracellular Delivery and Sampling of Biomolecules from Live Cells.

Nondestructive cell membrane permeabilization systems enable the intracellular delivery of exogenous biomolecules for cell engineering tasks as well as the temporal sampling of cytosolic contents from live cells for the analysis of dynamic processes. Here, we report a microwell array format live-cell analysis device (LCAD) that can perform localized-electroporation induced membrane permeabilization, for cellular delivery or sampling, and directly interfaces with surface-based biosensors for analyzing the extracted contents. We demonstrate the capabilities of the LCAD via an automated high-throughput workflow for multimodal analysis of live-cell dynamics, consisting of quantitative measurements of enzyme activity using self-assembled monolayers for MALDI mass spectrometry (SAMDI) and deep-learning enhanced imaging and analysis. By combining a fabrication protocol that enables robust assembly and operation of multilayer devices with embedded gold electrodes and an automated imaging workflow, we successfully deliver functional molecules (plasmid and siRNA) into live cells at multiple time-points and track their effect on gene expression and cell morphology temporally. Furthermore, we report sampling performance enhancements, achieving saturation levels of protein tyrosine phosphatase activity measured from as few as 60 cells, and demonstrate control over the amount of sampled contents by optimization of electroporation parameters using a lumped model. Lastly, we investigate the implications of cell morphology on electroporation-induced sampling of fluorescent molecules using a deep-learning enhanced image analysis workflow.

Recommendations for a More Organized and Effective Approach to the Early Detection of Pancreatic Cancer From the PRECEDE (Pancreatic Cancer Early Detection) Consortium

Recommendations for a More Organized and Effective Approach to the Early Detection of Pancreatic Cancer From the PRECEDE (Pancreatic Cancer Early Detection) Consortium

Dual modality neutron and x-ray tomography for enhanced image analysis of the bone-metal interface

The bone tissue formed at the contact interface with metallic implants, particularly its 3D microstructure, plays a pivotal role for the structural integrity of implant fixation. X-ray tomography is the classical imaging technique used for accessing microstructural information from bone tissue. However, neutron tomography has shown promise for visualising the immediate bone-metal implant interface, something which is highly challenging with x-rays due to large differences in attenuation between metal and biological tissue causing image artefacts. To highlight and explore the complementary nature of neutron and x-ray tomography, proximal rat tibiae with titanium-based implants were imaged with both modalities. The two techniques were compared in terms of visualisation of different material phases and by comparing the properties of the individual images, such as the contrast-to-noise ratio. After superimposing the images using a dedicated image registration algorithm, the complementarity was further investigated via analysis of the dual modality histogram, joining the neutron and x-ray data. From these joint histograms, peaks with well-defined grey value intervals corresponding to the different material phases observed in the specimens were identified and compared. The results highlight differences in how neutrons and x-rays interact with biological tissues and metallic implants, as well as the benefits of combining both modalities. Future refinement of the joint histogram analysis could improve the segmentation of structures and tissues, and yield novel information about specimen-specific properties such as moisture content.

Applying Artificial Intelligence to Gynecologic Oncology: A Review.

Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. The aim of this study was to review the role of AI in gynecologic oncology. Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology. With advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged. In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.

A phenomenological criterion for an optical assessment of PE-HD fracture surfaces obtained from FNCT

The full-notch creep test (FNCT) is a common test method to evaluate the environmental stress cracking (ESC) behavior of high-density polyethylene (PE-HD), e.g. for container materials. The test procedure as specified in ISO 16770 provides a comparative measure of the resistance against ESC using the time to failure of PE-HD specimens under constant mechanical load in a well-defined liquid test environment. Since the craze-crack damage mechanism underlying the ESC phenomenon is associated with brittle failure, the occurrence of a predominantly brittle fracture surface is a prerequisite to consider an FNCT measurement as representative for ESC, i.e. a time to failure dominated by craze-crack propagation.The craze-crack propagation continuously reduces the effective residual cross-sectional area of the specimen during the test, which results in a corresponding increase of the effective mechanical stress. Thus, a transition to ductile shear deformation is inevitable at later stages of the test, leading usually to a pronounced central ligament.Therefore, an optical evaluation of FNCT fracture surfaces concerning their brittleness is essential. An enhanced imaging analysis of FNCT fracture surfaces enables a detailed assessment of craze-crack propagation during ESC. In this study, laser scanning microscopy (LSM) was employed to evaluate whether FNCT fracture surfaces are representative with respect to craze-crack propagation and ESC. Based on LSM height data, a phenomenological criterion is proposed to assess the validity of distinct FNCT measurements. This criterion is supposed to facilitate a quick evaluation of FNCT results in practical routine testing. Its applicability is verified on a sample basis for seven different commercial PE-HD container materials.

Clinical Implications of Machine Learning, Artificial Intelligence, and Radiomics in Cardiac Imaging

Rapid advancements in technology and electronic medical record systems have given rise to massive amounts of cardiac imaging data with the potential to alter medical practices. The rise of machine learning (ML) and radiomics – the concept that images contain invaluable data regarding disease processes beyond what the eyes can see – promises increased precision and accuracy to the current standard of care. Recent advancements in major cardiac imaging modalities, such as echocardiography, cardiac CT and cardiac MRI, have uncovered promising diagnostic and prognostic information through the application of ML. In echocardiography, ML has been successfully applied to identify views, make right- and left-sided heart measurements, and detect certain diseases like hypertrophic cardiomyopathy. Application of ML in cardiac CT has seen success in quantifying coronary plaque burden, identifying significant coronary stenosis, and predicting mortality in coronary artery disease (CAD). In cardiac MRI, efforts have been made to automatize segmentation for chamber measurements and detecting fibrosis. For nuclear imaging, ML has been applied to not only make measurements like the left ventricular ejection fraction, but also to identify perfusion abnormalities and predict obstructive CAD. While there are many milestones still to be reached before ML can be widely integrated to current clinical practices, there is optimism for ML to advance the field of cardiovascular imaging through enhanced image analysis and improved efficiency.

CT imaging and quantitative analysis in the differential diagnosis of hepatic epithelioid hemangioendothelioma and hepatic metastasis

Objective To study the use of abdominal enhanced CT imaging and quantitative index analysis in the differential diagnosis of hepatic epithelioid hemangioendothelioma (HEH) and hepatic metastasis. Methods A study group of 12 patients with HEH who underwent abdominal enhanced CT scanning at the First Affiliated Hospital of Zhengzhou University from February 2014 to October 2018 was retrospectively compared with a control group of 52 patients with hepatic metastases diagnosed clinically and by imaging examinations. The general information and imaging data of these patients were collected and analyzed. Results The lesions in the 2 groups mainly presented as multiple and diffuse lesions. The diffuse lesions of HEH often fused into strips. The hepatic metastasis group showed a higher CT attenuation and TNR in the portal vein phase than the HEH group (P<0.05). The area under the ROC curves of the two indexes were 0.756 and 0.841 respectively. The centers of the lesions showed almost no or slightly homogeneous enhancement in the HEH group, while the liver metastasis group showed slightly and moderately heterogeneous enhancement, with a significant difference between the two groups (P<0.05). Female, subcapsular distribution, capsular contraction, target ring sign and lollipop sign were independent risk factors for HEH (P<0.05), while a high CT attenuation and TNR in the portal vein phase, elevated tumor markers and lymph node metastasis were independent risk factors for liver metastasis on logistic regression analysis (P<0.05). Conclusions CT attenuation, TNR, central enhancement features in the portal vein phase, special signs and secondary changes of lesions were helpful for the differential diagnosis between HEH and liver metastasis. Key words: Hemangioendothelioma, epithelioid; Hepatoma; Tomography, X-ray computed; Tumor metastasis; Differential diagnosis

New Methods for Cell Cycle Analysis

Abstract Cell cycle is a basic process during the cell life. Analyzing cell cycles can better understand the nature of diseases. Currently, cell cycles are mainly analyzed by flow cytometry to investigate intracellular DNA contents and cell cycle specific proteins. With the development of technology, several new methods have emerged for cell cycle analysis in recent years to improve and enhance image analysis, single-cell analysis, sample reduction, and high throughput. These new methods include image flow cytometry, mass flow cytometry, microfluidic technology, and Raman spectroscopy, which have greatly expanded the methods for cell cycle analysis and increased the accuracy of cell cycle measurements. Here we briefly introduce these new methods for cell cycle analysis.