Quantitative microstructure analysis of polymer-modified mortars

Digital light, fluorescence and electron microscopy in combination with wavelength-dispersive spectroscopy were used to visualize individual polymers, air voids, cement phases and filler minerals in a polymer-modified cementitious tile adhesive. In order to investigate the evolution and processes involved in formation of the mortar microstructure, quantifications of the phase distribution in the mortar were performed including phase-specific imaging and digital image analysis. The required sample preparation techniques and imaging related topics are discussed. As a form of case study, the different techniques were applied to obtain a quantitative characterization of a specific mortar mixture. The results indicate that the mortar fractionates during different stages ranging from the early fresh mortar until the final hardened mortar stage. This induces process-dependent enrichments of the phases at specific locations in the mortar. The approach presented provides important information for a comprehensive understanding of the functionality of polymer-modified mortars.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.

Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Methods for preparing and counting biochemical varves

Four different preparation and counting methods for biochemical varves were compared in order to assess counting errors and to standardize these techniques. The properties of two embedding methods, namely the shock-freeze, freeze-dry and the water-acetone-epoxy-exchange method, are discussed. Varve counts were carried out on fresh sediment and on sediment thin-sections, on the latter by manual and by automated counting using image-analysis software. Counting on fresh sediment and using image-analysis generally underestimated the number of varves, especially in sections with inconspicuous varves. A comparison between multiple varve counts carried out by a single analyst and different analysts showed no significant differences in the mean varve counts.

Predicting and monitoring cancer treatment response with diffusion-weighted MRI

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response.

Effect of systemic tetracycline on the degradation of tetracycline-impregnated bilayered collagen membranes: an animal study

BACKGROUND: Premature collagen membrane degradation may compromise the outcome of osseous regenerative procedures. Tetracyclines (TTCs) inhibit the catalytic activities of human metalloproteinases. Preprocedural immersion of collagen membranes in TTC and systemic administration of TTC may be possible alternatives to reduce the biodegradation of native collagen membranes. AIM: To evaluate the in vivo degradation of collagen membranes treated by combined TTC immersion and systemic administration. MATERIALS AND METHODS: Seventy-eight bilayered porcine collagen membrane disks were divided into three groups and were immersed in 0, 50, or 100 mg/mL TTC solution. Three disks, one of each of the three groups, were implanted on the calvaria of each of 26 Wistar rats. Thirteen (study group) were administered with systemic TTC (10 mg/kg), while the remaining 13 received saline injections (control group). Calvarial tissues were retrieved after 3 weeks, and histological sections were analyzed by image analysis software. RESULTS: Percentage of remaining collagen area within nonimpregnated membranes was 52.26 ± 20.67% in the study group, and 32.74 ± 13.81% in the control group. Immersion of membranes in 100 mg/mL TTC increased the amount of residual collagen to 63.46 ± 18.19% and 42.82 ± 12.99% (study and control groups, respectively). Immersion in 50 mg/mL TTC yielded maximal residual collagen values: 80.75 ± 14.86% and 59.15 ± 8.01% (study and control groups, respectively). Differences between the TTC concentrations, and between the control and the study groups were statistically significant. CONCLUSIONS: Immersion of collagen membranes in TTC solution prior to their implantation and systemic administration of TTC significantly decreased the membranes' degradation.

Delayed osteoblast differentiation and altered inflammatory response around implants placed in incisor sockets of type 2 diabetic rats

Objective: Central to the process of osseointegration is the recruitment of mesenchymal progenitor cells to the healing site, their proliferation and differentiation to bone synthesising osteoblasts. The process is under the control of pro-inflammatory cytokines and growth factors. The aim of this study was to monitor these key stages of osseointegration and the signalling milieu during bone healing around implants placed in healthy and diabetic bone. Methods: Implants were placed into the sockets of incisors extracted from the mandibles of normal Wistar and diabetic Goto-Kakizaki rats. Mandibles 1-12 weeks post-insertion of the implant were examined by histochemistry and immunocytochemistry to localise the presence of Stro-1- positive mesenchymal progenitor cells, proliferating cellular nuclear antigen proliferative cells, osteopontin and osteocalcin, macrophages, pro-inflammatory cytokines interleukin (IL)-1 , IL-6, tumour necrosis factor (TNF)- and tumour growth factor (TGF)- 1. Image analysis provided a semi-quantification of positively expressing cells. Results: Histological staining identified a delay in the formation of mineralised bone around implants placed in diabetic animals. Within the diabetic bone, the migration of Stro-1 mesenchymal cells in the healing tissue appeared to be unaffected. However, in the diabetic healing bone, the onset of cell proliferation and osteoblast differentiation were delayed and subsequently prolonged compared with normal bone. Similar patterns of change were observed in diabetic bone for the presence of IL-1 , TNF- , macrophages and TGF- 1. Conclusion: The observed alterations in the extracellular presence of pro-inflammatory cytokines, macrophages and growth factors within diabetic tissues that correlate to changes in the signalling milieu, may affect the proliferation and differentiation of mesenchymal progenitor cells in the osseointegration process. To cite this article: Colombo JS, Balani D, Sloan AJ, St Crean J, Okazaki J, Waddington RJ. Delayed osteoblast differentiation and altered inflammatory response around implants placed in incisor sockets of type 2 diabetic rats Clin. Oral Impl. Res22, 2011; 578-586 doi: 10.1111/j.1600-0501.2010.01992.x.

Development of a biocrystallisation assay for examining effects of homeopathic preparations ursing cress seedlings

A major challenge in basic research into homeopathic potentisation is to develop bioassays that yield consistent results. We evaluated the potential of a seedling-biocrystallisation method. Cress seeds (Lepidium sativum L.) germinated and grew for 4 days in vitro in Stannum metallicum 30x or water 30x in blinded and randomized assignment. 15 experiments were performed at two laboratories. CuCl2-biocrystallisation of seedlings extracted in the homeopathic preparations was performed on circular glass plates. Resulting biocrystallograms were analysed by computerized textural image analysis. All texture analysis variables analysed yielded significant results for the homeopathic treatment; thus the texture of the biocrystallograms of homeopathically treated cress exhibited specific characteristics. Two texture analysis variables yielded differences between the internal replicates, most probably due to a processing order effect. There were only minor differences between the results of the two laboratories. The biocrystallisation method seems to be a promising complementary outcome measure for plant bioassays investigating effects of homeopathic preparations.

Cerebral correlates of muscle tone fluctuations in restless legs syndrome: A pilot study with combined functional magnetic resonance imaging and anterior tibial muscle electromyography

BACKGROUND: The pathology of restless legs syndrome (RLS) is still not understood. To investigate the pathomechanism of the disorder further we recorded a surface electromyogram (EMG) of the anterior tibial muscle during functional magnetic resonance imaging (fMRI) in patients with idiopathic RLS. METHODS: Seven subjects with moderate to severe RLS were investigated in the present pilot study. Patients were lying supine in the scanner for over 50min and were instructed not to move voluntarily. Sensory leg discomfort (SLD) was evaluated on a 10-point Likert scale. For brain image analysis, an algorithm for the calculation of tonic EMG values was developed. RESULTS: We found a negative correlation of tonic EMG and SLD (p <0.01). This finding provides evidence for the clinical experience that RLS-related subjective leg discomfort increases during muscle relaxation at rest. In the fMRI analysis, the tonic EMG was associated with activation in motor and somatosensory pathways and also in some regions that are not primarily related to motor or somatosensory functions. CONCLUSIONS: By using a newly developed algorithm for the investigation of muscle tone-related changes in cerebral activity, we identified structures that are potentially involved in RLS pathology. Our method, with some modification, may also be suitable for the investigation of phasic muscle activity that occurs during periodic leg movements.