Tinkerbell—A Tool for Interactive Segmentation of 3D Data

A software system for interactive manipulation of three-dimensional data has been developed, based on the Open Inventor tool kit. The primary use of this software system is in the segmentation of tomographic reconstructions of subcellular structures. To this end, the reconstruction is represented by volume rendering and displayed in stereo. A three-dimensional cursor with adjustable shape and size is used to define and isolate regions of interest inside the volume, based on the user's expert knowledge. Once isolated, the region of interest can be conveniently analyzed and displayed.     

三维超声心脏图像的模糊聚类分割

采用三维超声心动图对小儿先天性心脏病进行诊断与治疗能达到比传统二维超声心动图更直观的效果。然而由于超声图像质量较差,三维超声心动图的可视化效果往往无法达到医生的要求。本文对三维超声心脏图像进行分割,以改进超声图像的可视化效果,并为参数提取等提供基础。首先采用快速的模糊c均值聚类得到初始分割结果;然后利用图像多分辨率技术进行修正;接着结合图像的对比度进行进一步的分割;最后,把处理后的图像用绘制的方法显示出来。本文的结果对超声图像的可视化效果有一定的改善.     

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

ObjectivesThe accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.Materials and MethodsTo address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.ResultsWe compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).ConclusionExperimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.     

Engineering 3D Cellularized Collagen Gels for Vascular Tissue Regeneration

Synthetic materials are known to initiate clinical complications such as inflammation, stenosis, and infections when implanted as vascular substitutes. Collagen has been extensively used for a wide range of biomedical applications and is considered a valid alternative to synthetic materials due to its inherent biocompatibility (i.e., low antigenicity, inflammation, and cytotoxic responses). However, the limited mechanical properties and the related low hand-ability of collagen gels have hampered their use as scaffold materials for vascular tissue engineering. Therefore, the rationale behind this work was first to engineer cellularized collagen gels into a tubular-shaped geometry and second to enhance smooth muscle cells driven reorganization of collagen matrix to obtain tissues stiff enough to be handled.The strategy described here is based on the direct assembling of collagen and smooth muscle cells (construct) in a 3D cylindrical geometry with the use of a molding technique. This process requires a maturation period, during which the constructs are cultured in a bioreactor under static conditions (without applied external dynamic mechanical constraints) for 1 or 2 weeks. The “static bioreactor” provides a monitored and controlled sterile environment (pH, temperature, gas exchange, nutrient supply and waste removal) to the constructs. During culture period, thickness measurements were performed to evaluate the cells-driven remodeling of the collagen matrix, and glucose consumption and lactate production rates were measured to monitor the cells metabolic activity. Finally, mechanical and viscoelastic properties were assessed for the resulting tubular constructs. To this end, specific protocols and a focused know-how (manipulation, gripping, working in hydrated environment, and so on) were developed to characterize the engineered tissues.     

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.     

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods,Datasets, Libraries,and Future Directions

ObjectivesAccurate and reliable segmentation of brain tumors from MRI images helps in planning an enhanced treatment and increases the life expectancy of patients. However, the manual segmentation of brain tumors is subjective and more prone to errors. Nonetheless, the recent advances in convolutional neural network (CNN)-based methods have exhibited outstanding potential in robust segmentation of brain tumors. This article comprehensively investigates recent advances in CNN-based methods for automatic segmentation of brain tumors from MRI images. It examines popular deep learning (DL) libraries/tools for an expeditious and effortless implementation of CNN models. Furthermore, a critical assessment of current DL architectures is delineated along with the scope of improvement.MethodsIn this work, more than 50 scientific papers from 2014-2020 are selected using Google Scholar and PubMed. Also, the leading journals related to our work along with proceedings from major conferences such as MICCAI, MIUA and ECCV are retrieved. This research investigated various annual challenges too related to this work including Multimodal Brain Tumor Segmentation Challenge (MICCAI BRATS) and Ischemic Stroke Lesion Segmentation Challenge (ISLES).ResultAfter a systematic literature search pertinent to the theme, we found that principally there exist three variations of CNN architecture for brain tumor segmentation: single-path and multi-path, fully convolutional, and cascaded CNNs. The respective performances of most automated methods based on CNN are appraised on the BraTS dataset, provided as a part of the MICCAI Multimodal Brain Tumor Segmentation challenge held annually since 2012.ConclusionNotwithstanding the remarkable potential of CNN-based methods, reliable and robust segmentation of brain tumors continues to be an intractable challenge. This is due to the intricate anatomy of the brain, variability in its appearance, and imperfection in image acquisition. Moreover, owing to the small size of MRI datasets, CNN-based methods cannot operate with their full capacity, as demonstrated with large scale datasets, such as ImageNet.     

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.     

Steerable3D: An ImageJ plugin for neurovascular enhancement in 3-D segmentation

• Purpose
• Image processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.
• Methods
• The developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.
• Results
• The filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.
• Conclusion
• The developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images.

     

Interweaving 3D Network Binder for High‐Areal‐Capacity Si Anode through Combined Hard and Soft Polymers

Si anodes suffer an inherent volume expansion problem. The consensus is that hydrogen bonds in these anodes are preferentially constructed between the binder and Si powder for enhanced adhesion and thus can improve cycling performance. There has been little research done in the field of understanding the contribution of the binder's mechanical properties to performance. Herein, a simple but effective strategy is proposed, combining hard/soft polymer systems, to exploit a robust binder with a 3D interpenetrating binding network (3D‐IBN) via an in situ polymerization. The 3D‐IBN structure is constructed by interweaving a hard poly(furfuryl alcohol) as the skeleton with a soft polyvinyl alcohol (PVA) as the filler, buffering the dramatic volume change of the Si anode. The resulting Si anode delivers an areal capacity of >10 mAh cm^?2 and enables an energy density of >300 Wh kg^?1 in a full lithium‐ion battery (LIB) cell. The component of the interweaving binder can be switched to other polymers, such as replacing PVA by thermoplastic polyurethane and styrene butadiene styrene. Such a strategy is also effective for other high‐capacity electroactive materials, e.g., Fe₂O₃ and Sn. This finding offers an alternative approach in designing high‐areal‐capacity electrodes through combined hard and soft polymer binders for high‐energy‐density LIBs.     

Using digital images to reconstruct three‐dimensional biological forms: a new tool for morphological studies

We examined two image‐based methods, photogrammetry and stereo vision, used for reconstructing the three‐dimensional form of biological organisms under field conditions. We also developed and tested a third ‘hybrid’ method, which combines the other two techniques. We tested these three methodologies using two different cameras to obtain digital images of museum and field sampled specimens of giant tortoises. Both the precision and repeatability of the methods were assessed statistically on the same specimens by comparing geodesic and Euclidean measurements made on the digital models with linear measurements obtained with caliper and flexible tape. We found no substantial difference between the three methods in measuring the Euclidean distance between landmarks, but spatially denser models (stereo vision and ‘hybrid’) were more accurate for geodesic distances. The use of different digital cameras did not influence the results. Image‐based methods require only inexpensive instruments and appropriate software, and allow reconstruction of the three‐dimensional forms (including their curved surfaces) of organisms sampled in the field. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 95 , 425–436.     

Ultrathin and Porous Ni3S2/CoNi2S4 3D‐Network Structure for Superhigh Energy Density Asymmetric Supercapacitors

3D‐networked, ultrathin, and porous Ni₃S₂/CoNi₂S₄ on Ni foam (NF) is successfully designed and synthesized by a simple sulfidation process from 3D Ni–Co precursors. Interestingly, the edge site‐enriched Ni₃S₂/CoNi₂S₄/NF 3D‐network is realized by the etching‐like effect of S^2? ions, which made the surfaces of Ni₃S₂/CoNi₂S₄/NF with a ridge‐like feature. The intriguing structural/compositional/componental advantages endow 3D‐networked‐free‐standing Ni₃S₂/CoNi₂S₄/NF electrodes better electrochemical performance with specific capacitance of 2435 F g^?1 at a current density of 2 A g^?1 and an excellent rate capability of 80% at 20 A g^?1. The corresponding asymmetric supercapacitor achieves a high energy density of 40.0 W h kg^?1 at an superhigh power density of 17.3 kW kg^?1, excellent specific capacitance (175 F g^?1 at 1A g^?1), and electrochemical cycling stability (92.8% retention after 6000 cycles) with Ni₃S₂/CoNi₂S₄/NF as the positive electrode and activated carbon/NF as the negative electrode. Moreover, the temperature dependences of cyclic voltammetry curve polarization and specific capacitances are carefully investigated, and become more obvious and higher, respectively, with the increase of test temperature. These can be attributed to the components' synergetic effect assuring rich redox reactions, high conductivity as well as highly porous but robust architectures. This work provides a general, low‐cost route to produce high performance electrode materials for portable supercapacitor applications on a large scale.     

Vascular smooth muscle cell proliferation as a therapeutic target. Part 1: molecular targets and pathways

Cardiovascular diseases are a major cause of human death worldwide. Excessive proliferation of vascular smooth muscle cells contributes to the etiology of such diseases, including atherosclerosis, restenosis, and pulmonary hypertension. The control of vascular cell proliferation is complex and encompasses interactions of many regulatory molecules and signaling pathways. Herein, we recapitulated the importance of signaling cascades relevant for the regulation of vascular cell proliferation. Detailed understanding of the mechanism underlying this process is essential for the identification of new lead compounds (e.g., natural products) for vascular therapies.     

Cytokines, macrophage lipid metabolism and foam cells: implications for cardiovascular disease therapy

Cardiovascular disease is the biggest killer globally and the principal contributing factor to the pathology is atherosclerosis; a chronic, inflammatory disorder characterized by lipid and cholesterol accumulation and the development of fibrotic plaques within the walls of large and medium arteries. Macrophages are fundamental to the immune response directed to the site of inflammation and their normal, protective function is harnessed, detrimentally, in atherosclerosis. Macrophages contribute to plaque development by internalizing native and modified lipoproteins to convert them into cholesterol-rich foam cells. Foam cells not only help to bridge the innate and adaptive immune response to atherosclerosis but also accumulate to create fatty streaks, which help shape the architecture of advanced plaques. Foam cell formation involves the disruption of normal macrophage cholesterol metabolism, which is governed by a homeostatic mechanism that controls the uptake, intracellular metabolism, and efflux of cholesterol. It has emerged over the last 20 years that an array of cytokines, including interferon-γ, transforming growth factor-β1, interleukin-1β, and interleukin-10, are able to manipulate these processes. Foam cell targeting, anti-inflammatory therapies, such as agonists of nuclear receptors and statins, are known to regulate the actions of pro- and anti-atherogenic cytokines indirectly of their primary pharmacological function. A clear understanding of macrophage foam cell biology will hopefully enable novel foam cell targeting therapies to be developed for use in the clinical intervention of atherosclerosis.