Quantitative analysis of scoliosis X-ray images based on Keypoint R-CNN

Advancements in genomic, computing, and imaging technology have spurred new opportunities to use quantitative image analysis (QIA) for diagnostic testing. To develop evidence-based recommendations to improve accuracy, precision, and reproducibility in the interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) for breast cancer where QIA is used. The College of American Pathologists (CAP) convened a panel of pathologists, histotechnologists, and computer scientists with expertise in image analysis, immunohistochemistry, quality management, and breast pathology to develop recommendations for QIA of HER2 IHC in breast cancer. A systematic review of the literature was conducted to address 5 key questions. Final recommendations were derived from strength of evidence, open comment feedback, expert panel consensus, and advisory panel review. Eleven recommendations were drafted: 7 based on CAP laboratory accreditation requirements and 4 based on expert consensus opinions. A 3-week open comment period received 180 comments from more than 150 participants. To improve accurate, precise, and reproducible interpretation of HER2 IHC results for breast cancer, QIA and procedures must be validated before implementation, followed by regular maintenance and ongoing evaluation of quality control and quality assurance. HER2 QIA performance, interpretation, and reporting should be supervised by pathologists with expertise in QIA.

Classification of graphite particles in metallographic images of cast irons – Quantitative image analysis versus deep learning

Medical Imaging devices facilitates enormous amount of anatomical and functional information, which aids in improved diagnosis and patient care specifically when supported by quantitative image analysis techniques. MR image pre-processing is an elementary step to ensure the favorable outcome of quantitative image analysis pipeline. The preprocessing step composes of different processes aimed to either improve the image quality or to standardize its intensity patterns. Regardless of modality specific image artefacts occurrence of intensity inhomogeneity in Magnetic Resonance Images (MRI) is prevalent which adversely affect the quantitative image analysis. In this paper a pre-processing approach for MRI sequences is introduced, where in the intensity inhomogeneity is introduced during the image acquisition process. These intensity inhomogeneities due to varying magnetic fields affect the further image analysis. Therefore to correct these discrepancies an intensity inhomogeneity correction method is applied for different MRI brain tumor scans, N4 parameters i.e non parametric, nonuniform intensity normalization technique parameters are set with B-spline grid resolution of (1, 1, 1). B-spline cube and spline distance of. 10 and number of iterations are set to 50, 40, 30 and a convergence threshold of. 1001. The proposed method is evaluated with a standard dataset of BraTs 2017 and the results satisfactory.

The quality and interpretability of the state-of-the-art methods for automatic analysis of chest X-ray images is still not sufficient. We address this problem by presenting a model that combines the analysis of frontal chest X-ray scans with structured patient information contained within radiology records. The proposed model generates a short textual summary with essential information on the found pathologies along with their location and severity; and the 2D heatmaps localizing each pathology on the original X-ray images. We test the proposed model on the MIMIC-CXR dataset. It achieves the state-of-the-art performance for image labelling and captioning (78.5% of correctly generated sentences) and defeats other similar solutions that dismiss the additional patient data (by 5.2% of correctly generated sentences). We also propose an automatic approach to label mining that leverages multimodal data: the X-ray images, related textual reports, patients' age and sex.

Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used ImageJ analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (a) the conventionally employed ImageJ software, and (b) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labeling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on poly(vinylidene difluoride) (PVDF) based polymeric substrates. In particular, we used a number of fluorescence microscopy images of these mouse myoblasts grown on PVDF-based nanobiocomposites under the influence of electric field. In addition to the MKS workflows requiring much less user time because of their automation, it was observed that ImageMKS workflows consistently produced more reliable results that correlated better with the previously reported experimental studies.

In this article we explore the relationship between semiotic analysis of images and quantitative analysis of vast image corpora, in particular the work produced by Lev Manovich and the Cultural Analytics Lab, called “Media Visualization.” Media Visualization has been chosen as corpus because of its metavisual operation (images are visualized and analyzed by images) and its innovating way of conceiving analysis: by visual instruments. In this paper semiotics is used as an approach to Media Visualization and taken as an object of study as well, especially visual semiotics. In this sense, a comparison between visual semiotics (close reading of small corpora) and quantitative analyses of images (distant reading of vast collections) are conducted from a semiotic point of view. Post-Greimassian semiotics guides this study with respect to the issue of the image-within-an-image and metavisual visualization; Peircean semiotics is employed to explain and develop the notion of diagram.

This paper presents a study on automatic diagnosis of pneumoconiosis by texture analysis of chest X-ray images. A pre-processing method for normalizing distribution of film density is proposed and its effectiveness is studied experimentally. Dominant causes of the deviations of texture features for evaluating the profusion of small opacities are excluded by the pre-processing. New texture features based on the distribution of gradient vectors are also proposed. Experiments to evaluate the proposed system are demonstrated.

Background Beta-2 transferrin (asialo transferrin) greater than 2-sialo transferrin is characteristic of cerebrospinal fluid (CSF). Our neurosurgery department recently requested availability of an in-house assay to evaluate post-surgical drainage fluids for presence of CSF. The Sebia Hydragel 6 beta-2 transferrin electrophoresis/immunofixation profile can be used as a qualitative (positive/negative) assay for this purpose. As a starting point for evaluation of the assay, we examined the sensitivity of the assay for detection of the beta-2 transferrin band in dilute CSF. This was performed by quantitative image analysis. Methods Assays were conducted according to manufacturer's instructions. Our proband sample was pooled CSF. Successive dilutions were by factors of 2 (1, 1/2, 1/4, 1/8, 1/16, 1/32). Electrophoresis/ immunofixation was performed simultaneously for these samples across lanes in 6-lane gels. We performed quantitative assessment of the electrophoretic bands for beta-2 transferrin by image analysis. The gel image was scanned to an electronic file (600 dpi png) using a desktop scanner. Grayscale pixel data were obtained using Python and the Python Image Library (PIL). Total grayscale integrals for staining of beta-2 transferrin bands were computed and compared across dilutions of the proband sample. Results For CSF dilutions, the assay was invariably visually positive down to dilutions of 1/16. By image analysis, this cutoff had signal/noise ratio (S/N) of 3.5. Across dilutions, band integrals were dependent on dilutions according to a simple hyperbola (representative example: see Figure). Conclusions The Sebia Hydragel 6 electrophoresis/immunofixation assay for qualitative assessment of presence of beta-2 transferrin was found to be suitable for unambiguous detection of beta-2 transferrin in CSF down to 1/16 dilution of CSF. By image analysis, the dose vs. response curve (density of staining for beta-2 transferrin across dilutions of CSF) was non-linear.

Alpha-methylacyl-CoA racemase (AMACR) is an enzyme involved in the metabolism of fatty acids and is an important tissue biomarker in the prostate to distinguish normal glands from prostate cancer. Here, for the first time, we evaluated the expression of AMACR protein in normal breast, ductal carcinoma in situ, and invasive carcinomas. By immunofluorescence and immunohistochemistry, AMACR was seen in cytoplasmic granules consistent with a mitochondrial and peroxisomal localization. AMACR expression was determined by immunohistochemistry on 160 invasive carcinomas with long follow-up, using a high-density tissue microarray, and evaluated by two methods: standard pathology review and quantitative image analysis. AMACR was overexpressed in 42 of 160 (26%) invasive carcinomas, and it was associated with a decrease in tumor differentiation, a feature of aggressive breast cancer. Quantitative analysis allowed for better discrimination and more accurate evaluation of low-intensity staining. In conclusion, AMACR protein is expressed in normal breast and its expression seems to increase in invasive carcinomas. We observed stronger AMACR protein expression in high-grade carcinomas when compared with low-grade ones. Quantitative image analysis is a novel way to accurately and reproducibly evaluate immunohistochemistry in breast tissue samples using high-density tissue microarrays.

ABSTRACTPurpose: To develop a sensitive scale of iris transillumination suitable for clinical and research use, with the capability of either quantitative analysis or visual matching of images.Methods: Iris transillumination photographic images were used from 70 study subjects with ocular or oculocutaneous albinism. Subjects represented a broad range of ocular pigmentation. A subset of images was subjected to image analysis and ranking by both expert and nonexpert reviewers. Quantitative ordering of images was compared with ordering by visual inspection. Images were binned to establish an 8-point scale. Ranking consistency was evaluated using the Kendall rank correlation coefficient (Kendall’s tau). Visual ranking results were assessed using Kendall’s coefficient of concordance (Kendall’s W) analysis.Results: There was a high degree of correlation among the image analysis, expert-based and non-expert-based image rankings. Pairwise comparisons of the quantitative ranking with each reviewer generated an average Kendall’s tau of 0.83 ± 0.04 (SD). Inter-rater correlation was also high with Kendall’s W of 0.96, 0.95, and 0.95 for nonexpert, expert, and all reviewers, respectively.Conclusions: The current standard for assessing iris transillumination is expert assessment of clinical exam findings. We adapted an image-analysis technique to generate quantitative transillumination values. Quantitative ranking was shown to be highly similar to a ranking produced by both expert and nonexpert reviewers. This finding suggests that the image characteristics used to quantify iris transillumination do not require expert interpretation. Inter-rater rankings were also highly similar, suggesting that varied methods of transillumination ranking are robust in terms of producing reproducible results.

Summary of discussion on nuclear enzymes

Abstract: The X-ray test is a simple, fast, non-destructive, and highly precise test that enables detailed examination of internal seed morphology and identification of seed damage and its location and type. The objective of the present study was to evaluate the efficiency of the X-ray test and the relationship between the structure of Crotalaria juncea seeds and germination. Six lots of Crotalaria juncea seeds of the IAC-KR1 cultivar were subjected to X-ray and germination tests. Normal and abnormal seedlings and dead seeds were photographed and analyzed together with the X-ray images of the seeds. Results showed that analysis of X-ray images is effective in identifying mechanical damage, stink bug damage, and deteriorated tissues in Crotalaria juncea seeds and is able to establish an association between their occurrence and germination.

Rheumatoid arthritis (RA) is a systemic life-threatening disorder and affects the body’s self immune system and invades the smaller and larger joints. There are different modern imaging techniques used such as magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT) and colour Doppler ultrasound were used to evaluate the inflammatory conditions in RA. Still radiography (X-ray) was investigated as the best method in diagnosis of RA. X-ray imaging has been the easiest and most used method of diagnosis of any abnormalities in the hard tissues of the human body. The study aimed as follows (i) to perform a manual and semi-automated segmentation of feet region in the total population studied. (ii) To compute the features extracted from the joint space width, joint space narrowing and erosion in the feet. The analysis of X-ray images was performed using manual method (DICOM viewer) and semi-automated method using Mimics software. The SPSS statistical analysis software was used to calculate mean standard deviation and standard deviation error and further plotted accordingly. There was a significant differences found between the manual and semi-automated methods. Furthermore, it was inferred that the semi-automated method showed more precise readings of the joint space narrowing in RA and normal feet and should be used more extensively for further study.

The GRAFIX I system was developed in the late 1960's as a fast flexible system for processing and analyzing filmed images, particularly of material which is essentially binary (black and white), such as printed text, line drawings, certain biomedical images, fingerprints, etc. It incorporates a large scale general purpose time shared computer to provide the facilities for the efficient development of algorithms necessary to perform various image processing and analysis tasks. In addition it contains a fast, high resolution flying-spot film scanner and a powerful and rather general slave processor (the binary image processor, or BIP) which provide data collection and manipulation facilities adequate to perform image processing and analysis tasks at commercially practical speeds. So far it has been successfully employed in a commercial environment to the reading of printed multifont text in complex page formats, and to the reading of Cyrillic, Greek and even handprinted text. At present we are considering future applications beyond the area of optical character recognition, particularly the analysis of engineering drawings, as well as the automatic analysis and classification of fingerprints, the analysis of biomedical images such as chromosomes, the analysis of x-ray images, and the analysis of satellite imagery data, among others.

Aim:This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays.Background:For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment.Objective:The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis.Methods:Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features.Results:An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making.Conclusion:According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.