Jiri Chmelik

Accurate micro-computed tomography imaging of pore spaces in collagen-based scaffold

In this work we have used X-ray micro-computed tomography (μCT) as a method to observe the morphology of 3D porous pure collagen and collagen-composite scaffolds useful in tissue engineering. Two aspects of visualizations were taken into consideration: improvement of the scan and investigation of its sensitivity to the scan parameters. Due to the low material density some parts of collagen scaffolds are invisible in a μCT scan. Therefore, here we present different contrast agents, which increase the contrast of the scanned biopolymeric sample for μCT visualization. The increase of contrast of collagenous scaffolds was performed with ceramic hydroxyapatite microparticles (HAp), silver ions (Ag+) and silver nanoparticles (Ag-NPs). Since a relatively small change in imaging parameters (e.g. in 3D volume rendering, threshold value and μCT acquisition conditions) leads to a completely different visualized pattern, we have optimized these parameters to obtain the most realistic picture for visual and qualitative evaluation of the biopolymeric scaffold. Moreover, scaffold images were stereoscopically visualized in order to better see the 3D biopolymer composite scaffold morphology. However, the optimized visualization has some discontinuities in zoomed view, which can be problematic for further analysis of interconnected pores by commonly used numerical methods. Therefore, we applied the locally adaptive method to solve discontinuities issue. The combination of contrast agent and imaging techniques presented in this paper help us to better understand the structure and morphology of the biopolymeric scaffold that is crucial in the design of new biomaterials useful in tissue engineering.

Combined bone lesion analysis in 3D CT data of vertebrae.

Two novel statistically based methods for bone lesion detection and classification are presented. Together with the previously published MRF method [15], they form a triad of mutually complementary methods that promise, when fused, to enable higher reliability of bone lesion assessment.

Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data

Our contribution presents a research progress in our long-term project that deals with spine analysis in computed tomography (CT) data. A fully automatic computer-aided diagnosis (CAD) system is presented, enabling the simultaneous segmentation and classification of metastatic tissues that can occur in the vertebrae of oncological patients. The task of the proposed CAD system is to segment metastatic lesions and classify them into two categories: osteolytic and osteoblastic. These lesions, especially osteolytic, are ill defined and it is difficult to detect them directly with only information about voxel intensity. The use of several local texture and shape features turned out to be useful for correct classification, however the exact determination of relevant image features is a difficult task. For this reason, the feature determination has been solved by automatic feature extraction provided by a deep convolutional neural network (CNN). The achieved mean sensitivity of detected lesions is greater than 92% with approximately three false positive detections per lesion for both types.

Fully Automatic CAD System for Spine Localisation and Vertebra Segmentation in CT Data

In this paper, we describe a fully automatic CAD system for spine detection in CT data followed by vertebra identification and segmentation. There are several basic problems: spine detection including the determination of spinal axis in spinal CT data, a localisation of individual vertebrae and identification of their types (order in spine) in case of incomplete scans of spine and also the final vertebra segmentation. By a subjective strict expert validation, the algorithm provides 82.6% of fully correct vertebra segmentations. Based on that, it seems to be routinely usable and fully applicable in preparation for the following automatic spine bone lesion analysis.

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data

HighlightsIndividual architecture of convolutional neural network.Patient and scan protocol dependent pre‐processing.Medial axis transform post‐processing for shape simplification of lesion candidates.Usability of the proposed method on whole‐spine scans (cervical, thoracic, lumbar). Graphical abstract Figure. No caption available. ABSTRACT This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill‐defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre‐processing steps that are dependent on a patient data and a scan protocol – it enables work with different types of CT scans; (ii) medial axis transform (MAT) post‐processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta‐analysis; and (iii) usability of the proposed method on whole‐spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies.

Spine lesion analysis in 3D CT data – Reporting on research progress

The contribution describes progress in the long-term project concerning automatic diagnosis of spine bone lesions. There are two difficult problems: segmenting reliably possibly severely deformed vertebrae in the spine and then detect, segment and classify the lesions that are often hardly visible thus making even the medical expert decisions highly uncertain, with a large inter-expert variety. New approaches are described enabling to solve both problems with a success rate acceptable for clinical testing, at the same time speeding up the process substantially compared to the previous stage. The results are compared with previously published achievements.

Modern Semi-automatic Set-up for Testing Cell Migration with Impact for Therapy of Myocardial Infarction

Ischemic heart disease and resulting acute myocardial infarction (AMI) is one of the main causes of morbidity and mortality in industrial countries. The idea for the modern therapeutic strategy, which should activate the migration of stem/progenitor cells or reduce the migration of inflammatory cells in AMI regions, has emerged in the last 15 years, mainly as a result of physiological observation and post-mortem histology. Published data from direct measurements of cell migration are very limited. We prepared a universal set-up that can be used for the testing of cell migration in AMI micro-environment. Mesenchymal stromal cells (MSCs), the most commonly used stem/progenitor cells in experimental cellular therapy for AMI, were used in the recent set-up tests. The cells, which should be tested for their migration potential, were injected into the starting point in a special micro-chamber on the substrate, and optics of the microscope allowed a time-lapse recording of cells in micrometre resolution every 2 min. Our software tools provided precise 2D and 3D tracking of moving cells and data export for statistical analysis. Set-up should be upgraded to a fully-automatic preclinical screening tool in the future.

Characterization of cells migration through cardiac tissue using advanced microscopy techniques and matlab simulation

Mesenchymal stromal cells (MSC) and neutrophils (NP) migration are important factors of the postinfarcted hearts remodeling. These both types of the cells can migrate through cardiac extracellular matrix to the central ischemic region. The quantitative description of MSC and NP migration through collagen matrix is important aim of modern bio-medicine. NP and MSC migration through peri-infarct zone was simulated in a custom-made microphantom: two chambers (bottom 10×20 mm) connected by a collagen tunnel. Bottom of the system was constructed from glass plate, compatible with confocal microscopy. System was heated at 37°C in 5-21 % O2 environment. The first chamber was starting point of migrating MSC and NP. The second chamber included living or apoptotic myocytes (model of central infarcted). Monitoring of migrating cells was performed on the confocal laser scanning microscope Leica. Chemotaxis movement of MSC through collagen tunnel between two chambers was approved. Speed was significantly modulated by collagen fiber orientation and hypoxic condition. The speed constants of cell motility were quantified by originally-made microphantom Matlab utility and basic equations for cell motility was proposed, usable for future creating of in-silico simulator of real cell invasivity in patients.