Jakub Segen

Bio: Jakub Segen is an academic researcher from Polish-Japanese Academy of Information Technology. The author has contributed to research in topics: Camera resectioning & Support vector machine. The author has an hindex of 3, co-authored 7 publications receiving 19 citations.

Registration of Ultrasound Images for Automated Assessment of Synovitis Activity

Abstract: Ultrasound images of joints are used by doctors to assess a degree of synovitis activity, in diagnosis and treatment of rheumatoid arthritis. Research on automation of synovitis assessment from ultrasound images is being conducted, with objectives of lowering medical costs and improving patients care. Analysis of synovitis area in an image should be done relative to the joint and bones, therefore the joint and bones must be located in the initial step. An approach is proposed for locating joint and bones, by registering structural descriptions of the joint region. A preliminary result is presented that includes a description of a registration method that iteratively improves the registration quality, and its application example based on synthetic data.

Optimization of Joint Detector for Ultrasound Images Using Mixtures of Image Feature Descriptors

Abstract: Joint detector is an essential part of an approach towards automated assessment of synovitis activity, which is a subject of the current research work. A recent formulation of the joint detector, that integrates image processing, local image neighborhood descriptors, such as SURF, FAST, ORB, BRISK, FREAK, trainable classification (SVM, NN, CART) and clusterization, results in a large number of possible choices of classifiers, their modes, components of features vectors, and parameter values, and making such choices by experimentation is impractical. This article presents a novel approach, and an implemented environment for the parameter selection process for the joint detector, which automatically choses the best configuration of image processing operators, type of image neighborhood descriptors, the form of a classifier and the clustering method and their parameters. Its implementation uses new scripting tools and generic techniques, such as chain-of-responsibility design pattern and metafunction idiom. Also presented are novel results, comparing the effect of feature vectors composed from multiple SURF descriptors on the performance of the joint detector, which demonstrate the potential of mixture of descriptors for improving the classification results.

Camera Calibration and Navigation in Networks of Rotating Cameras

Abstract: Camera calibration is one of the basic problems concerning intelligent video analysis in networks of multiple cameras with changeable pan and tilt (PT). Traditional calibration methods give satisfactory results, but are human labour intensive. In this paper we introduce a method of camera calibration and navigation based on continuous tracking, which requires minimal human involvement. After the initial pre-calibration, it allows the camera pose to be calculated recursively in real time on the basis of the current and previous camera images and the previous pose. The method is suitable if multiple coplanar points are shared between views from neighbouring cameras, which is often the case in the video surveillance systems.

Cellular Nuclei Differentiation Evaluated by Automated Analysis of CLSM Images

Abstract: Nuclear morphology abnormalities in cells are often the symptom of the cell death. However, minor disturbances in nuclear shape, such as a slight blebbing or deformation, may indicate characteristic medical disorders or therapeutic effect. The analysis of microscopic images requires time consuming observations and meticulous analysis that often are encumbered with human mistake. In the present work, image analysis of nuclei as a method of morphological verification is presented. The automated analysis of numerous cellular nuclei images may be a useful tool for any biological or analytical laboratory.

Cellular nuclei differentiation evaluated by automated analysis of CLSM images

Abstract: Nuclear morphology abnormalities in cells are often the symptom of the cell death. However, minor disturbances in nuclear shape, such as a slight blebbing or deformation, may indicate characteristic medical disorders or therapeutic effect. The analysis of microscopic images requires time consuming observations and meticulous analysis that often are encumbered with human mistake. In the present work, image analysis of nuclei as a method of morphological verification is presented. The automated analysis of numerous cellular nuclei images may be a useful tool for any biological or analytical laboratory.

The value of ultrasonography in predicting arthritis in auto-antibody positive arthralgia patients: a prospective cohort study

Abstract: IntroductionUltrasonography (US) has better sensitivity than clinical evaluation for the detection of synovitis in early rheumatoid arthritis (RA). Patients presenting with arthralgia and a positive anti-citrullinated protein antibodies (ACPA) and/or Rheumatoid Factor (IgM-RF) status are at risk for developing RA. In the present study, US utility and predictive properties in arthralgia patients at risk for the development of arthritis were studied.Methods192 arthralgia patients with ACPA and/or IgM-RF were included. Absence of clinical arthritis was confirmed by two physicians. US was performed by one of two trained radiologists of any painful joint, and of adjacent and contralateral joints. Joint effusion, synovitis and power Doppler (PD) signal in the synovial membrane of the joints and tenosynovitis adjacent to the joint were evaluated and classified on a 4-grade semi-quantitative scale. Grade 2-3 joint effusion, synovitis, tenosynovitis and grade 1-3 Power Doppler signal were classified as abnormal.ResultsForty-five patients (23%) developed arthritis after a mean of 11 months. Inter-observer reliability for synovitis and PD was moderate (kappa 0.46, and 0.56, respectively) and for joint effusion low (kappa 0.23). The prevalence of tenosynovitis was too low to calculate representative kappa values. At joint level, a significant association was found between US abnormalities and arthritis development in that joint for joint effusion, synovitis and PD. At patient level, a trend was seen towards more arthritis development in patients who had US abnormalities for joint effusion, synovitis, PD and tenosynovitis.ConclusionsUS abnormalities were associated with arthritis development at joint level, although this association did not reach statistical significance at patient level. US could potentially be used as a diagnostic tool for subclinical arthritis in seropositive arthralgia patients. However, further research is necessary to improve test characteristics.

Anemarrhenae asphodeloides rhizoma Extract Enriched in Mangiferin Protects PC12 Cells against a Neurotoxic Agent-3-Nitropropionic Acid.

Abstract: The rhizome of Anemarrhena asphodeloides Bunge, used in Traditional Chinese Medicine as a brain function-improving herb, is a promising source of neuroprotective substances. The aim of this study was to evaluate the protective action of xanthones from A. asphodeloides rhizomes on the PC12 cell line exposed to the neurotoxic agent-3-nitropropionic acid (3-NP). The xanthone-enriched fraction of the ethanolic extract of A. asphodeloides (abbreviated from now on as XF, for the Xanthone Fraction), rich in polyphenolic xanthone glycosides, in concentrations from 5 to 100 μg/mL, and 3-NP in concentrations from 2.5 to 15 mM, were examined. After 8, 16, 24, 48, and 72 h of exposure of cells to various combinations of 3-NP and XF, the MTT viability assay was performed and morphological changes were estimated by confocal fluorescence microscopy. The obtained results showed a significant increase in the number of cells surviving after treatment with XF with exposure to neurotoxic 3-NP and decreased morphological changes in PC12 cells in a dose and time dependent manner. The most effective protective action was observed when PC12 cells were pre-incubated with the XF. This effect may contribute to the traditional indications of this herb for neurological and cognitive complaints. However, a significant cytotoxicity observed at higher XF concentrations (over 10 µg/mL) and longer incubation time (48 h) requires caution in future research and thorough investigation into potential adverse effects.

Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods

Abstract: Objectives Rheumatoid arthritis is the most common rheumatic disease with arthritis, and causes substantial functional disability in approximately 50% patients after 10 years. Accurate measurement of the disease activity is crucial to provide an adequate treatment and care to the patients. The aim of this study is focused on a computer aided diagnostic system that supports an assessment of synovitis severity. Material and methods This paper focus on a computer aided diagnostic system that was developed within joint Polish-Norwegian research project related to the automated assessment of the severity of synovitis. Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Synovitis is estimated by ultrasound examiner using the scoring system graded from 0 to 3. Activity score is estimated on the basis of the examiner's experience or standardized ultrasound atlases. The method needs trained medical personnel and the result can be affected by a human error. Results The porotype of a computer-aided diagnostic system and algorithms essential for an analysis of ultrasonic images of finger joints are main scientific output of the MEDUSA project. Medusa Evaluation System prototype uses bone, skin, joint and synovitis area detectors for mutual structural model based evaluation of synovitis. Finally, several algorithms that support the semi-automatic or automatic detection of the bone region were prepared as well as a system that uses the statistical data processing approach in order to automatically localize the regions of interest. Conclusions Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Activity score is estimated on the basis of the examiner's experience and the result can be affected by a human error. In this paper we presented the MEDUSA project which is focused on a computer aided diagnostic system that supports an assessment of synovitis severity.

A Novel Method of Synovitis Stratification in Ultrasound Using Machine Learning Algorithms: Results From Clinical Validation of the MEDUSA Project

Abstract: Ultrasound is widely used in the diagnosis and follow-up of chronic arthritis. We present an evaluation of a novel automatic ultrasound diagnostic tool based on image recognition technology. Methods used in developing the algorithm are described elsewhere. For the purpose of evaluation, we collected 140 ultrasound images of metacarpophalangeal and proximal interphalangeal joints from patients with chronic arthritis. They were classified, according to hypertrophy size, into four stages (0–3) by three independent human observers and the algorithm. An agreement ratio was calculated between all observers and the standard derived from results of human staging using κ statistics. Results was significant in all pairs, with the highest p value of 3.9 × 10–6. κ coefficients were lower in algorithm/human pairs than between human assessors. The algorithm is effective in staging synovitis hypertrophy. It is, however, not mature enough to use in a daily practice because of limited accuracy and lack of color Doppler recognition. These limitations will be addressed in the future.