Efficient automatic 3D segmentation of cell nuclei for high-content screening.

BACKGROUND: High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. RESULTS: We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. CONCLUSIONS: The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs.

Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs.

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.

No-reference image quality assessment of magnetic resonance images with high-boost filtering and local features.

PURPOSE: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. METHODS: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve low-frequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. RESULTS: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. CONCLUSIONS: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development.

Interobserver variability in quality assessment of magnetic resonance images.

BACKGROUND: The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. METHODS: For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss' kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. RESULTS: The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. CONCLUSIONS: The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis.

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing-A Review.

Artificial intelligence (AI) is currently becoming a leading field in data processing [...].

Cell-Material Interplay in Focal Adhesion Points.

The complex interplay between cells and materials is a key focus of this research, aiming to develop optimal scaffolds for regenerative medicine. The need for tissue regeneration underscores understanding cellular behavior on scaffolds, especially cell adhesion to polymer fibers forming focal adhesions. Key proteins, paxillin and vinculin, regulate cell signaling, migration, and mechanotransduction in response to the extracellular environment. This study utilizes advanced microscopy, specifically the AiryScan technique, along with advanced image analysis employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) cluster algorithm, to investigate protein distribution during osteoblast cell adhesion to polymer fibers and glass substrates. During cell attachment to both glass and polymer fibers, a noticeable shift in the local maxima of paxillin and vinculin signals is observed at the adhesion sites. The focal adhesion sites on polymer fibers are smaller and elliptical but exhibit higher protein density than on the typical glass surface. The characteristics of focal adhesions, influenced by paxillin and vinculin, such as size and density, can potentially reflect the strength and stability of cell adhesion. Efficient adhesion correlates with well-organized, larger focal adhesions characterized by increased accumulation of paxillin and vinculin. These findings offer promising implications for enhancing scaffold design, evaluating adhesion to various substrates, and refining cellular interactions in biomedical applications.

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs.

Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.

Advances in Musculoskeletal Imaging and Their Applications.

Modern medical imaging systems provide ever-more information about the patient's health condition [...].

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys.

Currently, bone age is assessed by X-rays. It enables the evaluation of the child's development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. BACKGROUND: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. METHODS: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. RESULTS: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. CONCLUSIONS: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels.

BACKGROUND: Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS: Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS: Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS: There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging.

The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin's correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.

Active factor XI is associated with the risk of cardiovascular events in stable coronary artery disease patients.

BACKGROUND AND AIMS: Tissue factor (TF) and activated factor XI (FXIa) have been associated with acute coronary syndrome, ischemic stroke and venous thromboembolism. Their predictive value in stable coronary artery disease (CAD) is unclear. We investigated whether active TF and FXIa were associated with clinical outcomes in CAD patients in long-term observation. METHODS: In 124 stable patients with multivessel CAD, we assessed the presence of circulating, active TF and FXIa by measuring a response of thrombin generation to respective inhibitory antibodies. We recorded the composite endpoint of myocardial infarction (MI), stroke, systemic thromboembolism and cardiovascular death during follow-up (median 106 months, interquartile range 95-119). RESULTS: Circulating FXIa and active TF were detected in 40% and 20.8% of the 120 patients (aged 65.0 [57.0-70.3] years, men, 78.3%), who completed follow-up. The composite endpoint occurred more frequently in patients with detectable active TF and FXIa present at baseline (hazard ratio [HR] 4.02, 95% confidence interval [CI] 2.26-7.17, p < 0.001 and HR 6.21, 95% CI 3.40-11.40, p < 0.001, respectively). On multivariate analysis FXIa, but not active TF, was an independent predictor of the composite endpoint, as well as MI, stroke/systemic thromboembolism, and cardiovascular death, when analyzed separately. CONCLUSIONS: To our knowledge, this study is the first to show that circulating FXIa predicts arterial thromboembolic events in advanced CAD, supporting a growing interest in FXIa inhibitors as novel antithrombotic agents.

CT2TEE--a novel, internet-based simulator of transoesophageal echocardiography in congenital heart disease.

BACKGROUND: Echocardiography is one of the most important diagnostic tools in cardiology. The two most widespread modes of echocardiography are transthoracic (TTE) and transoesophageal (TEE) echocardiography, both requiring extensive training. In TEE the manual skills seem to be less important, but it is more challenging for the trainee to imagine the orientation of the cutting planes in space. This becomes more complicated, even for an experienced echocardiographer, in patients with congenital heart disease. There is a growing interest in TEE simulators. All of them are, however, manikin based and their use is limited to only a few medical centres. AIM: To develop an internet-based TEE simulator offering interactive training in cases with and without congenital heart disease. METHODS: Because of high spatial resolution we use data from ECG-triggered heart computed tomography (CT) to build the 3D heart model. On every CT image the oesophagus has to be marked and the greyscale values converted in order to mimic the tissue greyscales seen in TEE. After such preparations the 3D set created from CT data can be cut in any plane. The trainee can use the slider buttons in the simulator interface to freely steer the virtual TEE probe. While setting the desired TEE plane the application conducts calculations in order to simulate the typical sonographic artefacts. RESULTS: We were able to construct an application allowing a TEE simulation based on CT data. There are two versions of the simulator. The first one has to be downloaded to a personal computer together with the CT data. The second one is internet based and freely accessible on the project's web page (www.ct2tee.agh.edu.pl). It doesn't offer real time simulation, but is sufficient to obtain all possible views in the TEE. There are currently three data sets, two with congenital heart disease, and further development of the database and simulator is planned. CONCLUSIONS: The CT2TEE simulator, described in this paper, is the first fully interactive, internet-based TEE simulator. It can be a training tool both in learning TEE basics and in congenital heart disease.

Novel Technique for Growth Plate Analysis Based on the Superposition of T1- and T2-weighted MR Imaging of Adolescent Wrists.

In this article, a new method of information extraction on the basis of the differentiation of T1- and T2-weighted MR images is proposed. It relies on a technique of superposition of T1- and T2-weighted MR images with use of statistical dominance algorithm. On the basis of implemented image analysis, a reproducible extraction of growth zone of adolescent boys' wrists is possible.

Caries detection enhancement using texture feature maps of intraoral radiographs.

OBJECTIVES: Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation. METHODS: Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists. RESULTS: Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions. CONCLUSIONS: This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.

Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features.

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index.

CAS: Cell Annotation Software - Research on Neuronal Tissue Has Never Been so Transparent.

CAS (Cell Annotation Software) is a novel tool for analysis of microscopic images and selection of the cell soma or nucleus, depending on the research objectives in medicine, biology, bioinformatics, etc. It replaces time-consuming and tiresome manual analysis of single images not only with automatic methods for object segmentation based on the Statistical Dominance Algorithm, but also semi-automatic tools for object selection within a marked region of interest. For each image, a broad set of object parameters is computed, including shape features and optical and topographic characteristics, thus giving additional insight into data. Our solution for cell detection and analysis has been verified by microscopic data and its application in the annotation of the lateral geniculate nucleus has been examined in a case study.

Influence of applied corneal endothelium image segmentation techniques on the clinical parameters.

The corneal endothelium state is verified on the basis of an in vivo specular microscope image from which the shape and density of cells are exploited for data description. Due to the relatively low image quality resulting from a high magnification of the living, non-stained tissue, both manual and automatic analysis of the data is a challenging task. Although, many automatic or semi-automatic solutions have already been introduced, all of them are prone to inaccuracy. This work presents a comparison of four methods (fully-automated or semi-automated) for endothelial cell segmentation, all of which represent a different approach to cell segmentation; fast robust stochastic watershed (FRSW), KH method, active contours solution (SNAKE), and TOPCON ImageNET. Moreover, an improvement framework is introduced which aims to unify precise cell border location in images pre-processed with differing techniques. Finally, the influence of the selected methods on clinical parameters is examined, both with and without the improvement framework application. The experiments revealed that although the image segmentation approaches differ, the measures calculated for clinical parameters are in high accordance when CV (coefficient of variation), and CVSL (coefficient of variation of cell sides length) are considered. Higher variation was noticed for the H (hexagonality) metric. Utilisation of the improvement framework assured better repeatability of precise endothelial cell border location between the methods while diminishing the dispersion of clinical parameter values calculated for such images. Finally, it was proven statistically that the image processing method applied for endothelial cell analysis does not influence the ability to differentiate between the images using medical parameters.