scholarly journals Nanomaterials: Synthesis and Applications in Theranostics

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3228
Author(s):  
Gokul Paramasivam ◽  
Vishnu Vardhan Palem ◽  
Thanigaivel Sundaram ◽  
Vickram Sundaram ◽  
Somasundaram Chandra Kishore ◽  
...  
Keyword(s):  
State Of The Art ◽  
Imaging Systems ◽  
Distinct Advantage ◽  
Essential Properties ◽  
Nanomaterials Synthesis ◽  
Different Dimensions ◽  
Living Tissues ◽  
Therapy And Diagnosis ◽  
2D And 3D ◽  
Higher Sensitivity

Nanomaterials are endowed with unique features and essential properties suitable for employing in the field of nanomedicine. The nanomaterials can be classified as 0D, 1D, 2D, and 3D based on their dimensions. The nanomaterials can be malleable and ductile and they can be drawn into wires and sheets. Examples of nanomaterials are quantum dots (0D), nanorods, nanowires (1D), nanosheets (2D), and nanocubes (3D). These nanomaterials can be synthesized using top-down and bottom-up approaches. The achievements of 0D and 1D nanomaterials are used to detect trace heavy metal (e.g., Pb2+) and have higher sensitivity with the order of five as compared to conventional sensors. The achievements of 2D and 3D nanomaterials are used as diagnostic and therapeutic agents with multifunctional ability in imaging systems such as PET, SPECT, etc. These imaging modalities can be used to track the drug in living tissues. This review comprises the state-of-the-art of the different dimensions of the nanomaterials employed in theranostics. The nanomaterials with different dimensions have unique physicochemical properties that can be utilized for therapy and diagnosis. The multifunctional ability of the nanomaterials can have a distinct advantage that is used in the field of theranostics. Different dimensions of the nanomaterials would have more scope in the field of nanomedicine.

scholarly journals Challenges in the Fabrication of Biodegradable and Implantable Optical Fibers for Biomedical Applications

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 1972
Author(s):  
Agnieszka Gierej ◽  
Thomas Geernaert ◽  
Sandra Van Vlierberghe ◽  
Peter Dubruel ◽  
Hugo Thienpont ◽  
...  
Keyword(s):  
Systematic Review ◽  
Optical Fibers ◽  
Optical Waveguides ◽  
Biomedical Applications ◽  
State Of The Art ◽  
Chemical Properties ◽  
Biological Tissues ◽  
Biodegradable Materials ◽  
Essential Properties ◽  
Optically Transparent

The limited penetration depth of visible light in biological tissues has encouraged researchers to develop novel implantable light-guiding devices. Optical fibers and waveguides that are made from biocompatible and biodegradable materials offer a straightforward but effective approach to overcome this issue. In the last decade, various optically transparent biomaterials, as well as different fabrication techniques, have been investigated for this purpose, and in view of obtaining fully fledged optical fibers. This article reviews the state-of-the-art in the development of biocompatible and biodegradable optical fibers. Whilst several reviews that focus on the chemical properties of the biomaterials from which these optical waveguides can be made have been published, a systematic review about the actual optical fibers made from these materials and the different fabrication processes is not available yet. This prompted us to investigate the essential properties of these biomaterials, in view of fabricating optical fibers, and in particular to look into the issues related to fabrication techniques, and also to discuss the challenges in the use and operation of these optical fibers. We close our review with a summary and an outline of the applications that may benefit from these novel optical waveguides.

scholarly journals An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019 ◽  
Vol 9 (19) ◽  
pp. 4093 ◽  
Author(s):  
Santiago Royo ◽  
Maria Ballesta-Garcia
Keyword(s):  
Autonomous Vehicles ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Single Point ◽  
Imaging Systems ◽  
Light Sources ◽  
Final Solution ◽  
Large Uncertainty ◽  
Current State ◽  
The Subject

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

scholarly journals A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 5909-5916 ◽  
Author(s):  
Hao Zheng ◽  
Yizhe Zhang ◽  
Lin Yang ◽  
Peixian Liang ◽  
Zhuo Zhao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ensemble Learning ◽  
State Of The Art ◽  
3D Models ◽  
Superior Performance ◽  
3D Image ◽  
Convolutional Network ◽  
Biomedical Image ◽  
Learning Framework ◽  
2D And 3D

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the baselearners as multiple versions of “ground truths”. Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semisupervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.

scholarly journals A Uniform Architecture Design for Accelerating 2D and 3D CNNs on FPGAs

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 65 ◽  
Author(s):  
Zhiqiang Liu ◽  
Paul Chow ◽  
Jinwei Xu ◽  
Jingfei Jiang ◽  
Yong Dou ◽  
...  
Keyword(s):  
Design Space ◽  
State Of The Art ◽  
Three Dimensional ◽  
Architecture Design ◽  
Computer Vision Applications ◽  
Computationally Intensive ◽  
On Chip ◽  
3D Cnn ◽  
High Level ◽  
2D And 3D

Three-dimensional convolutional neural networks (3D CNNs) have gained popularity in many complicated computer vision applications. Many customized accelerators based on FPGAs are proposed for 2D CNNs, while very few are for 3D CNNs. Three-D CNNs are far more computationally intensive and the design space for 3D CNN acceleration has been further expanded since one more dimension is introduced, making it a big challenge to accelerate 3D CNNs on FPGAs. Motivated by the finding that the computation patterns of 2D and 3D CNNs are very similar, we propose a uniform architecture design for accelerating both 2D and 3D CNNs in this paper. The uniform architecture is based on the idea of mapping convolutions to matrix multiplications. A customized mapping module is developed to generate the feature matrix tilings with no need to store the entire enlarged feature matrix on-chip or off-chip, a splitting strategy is adopted to reconstruct a convolutional layer to adapt to the on-chip memory capacity, and a 2D multiply-and-accumulate (MAC) array is adopted to compute matrix multiplications efficiently. For demonstration, we implement an accelerator prototype with a high-level synthesis (HLS) methodology on a Xilinx VC709 board and test the accelerator on three typical CNN models: AlexNet, VGG16, and C3D. Experimental results show that the accelerator achieves state-of-the-art throughput performance on both 2D and 3D CNNs, with much better energy efficiency than the CPU and GPU.

scholarly journals Glass-based coatings on biomedical implants: a state-of-the-art review

2017 ◽  
Vol 3 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Francesco Baino ◽  
Enrica Verné
Keyword(s):  
Mechanical Properties ◽  
Bending Strength ◽  
State Of The Art ◽  
Antibacterial Properties ◽  
Porous Scaffolds ◽  
Bioactive Glasses ◽  
Metallic Substrates ◽  
Ocular Implants ◽  
Living Tissues ◽  
Joint Prostheses

AbstractBioactive glasses, invented by Prof. Larry L. Hench in the late 1960s, have revolutionized the field of biomaterials as they were shown to tightly bond to both hard and soft living tissues and to stimulate cells towards a path of regeneration and self-repair. However, due to their relatively poor mechanical properties (brittleness, low bending strength and fracture toughness), they are generally unsuitable for load-bearing applications. On the other hand, bioactive glasses have been successfully applied as coatings on the surface of stronger/tougher substrates to combine adequate mechanical properties with high bioactivity and, in some cases, additional extrafunctionalities (e.g. antibacterial properties, drug release). After giving a short overview of the main issues concerning the fabrication of glass coatings, this review provides a state-of-the-art picture in the field and specifically discusses the development of bioactive and hierarchical coatings on 3D porous scaffolds, joint prostheses, metallic substrates (e.g. wires or nails) for orthopedic fixation, polymeric meshes and sutures for wound healing, ocular implants and percutaneous devices.

Comparative Study of 2D and 3D Optical Imaging Systems: Laparoendoscopic Single-Site Surgery in an Ex Vivo Model

2017 ◽  
Vol 24 (6) ◽  
pp. 598-604 ◽  
Author(s):  
Jaime Vilaça ◽  
José Pedro Pinto ◽  
Sandra Fernandes ◽  
Patrício Costa ◽  
Jorge Correia Pinto ◽  
...  
Keyword(s):  
Comparative Study ◽  
Optical Imaging ◽  
Ex Vivo ◽  
Single Site ◽  
Imaging Systems ◽  
Ex Vivo Model ◽  
Laparoendoscopic Single Site ◽  
Laparoendoscopic Single Site Surgery ◽  
2D And 3D ◽  
Single Site Surgery

scholarly journals Specular Highlight Detection Based on Color Distribution for Endoscopic Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Baoxian Yu ◽  
Wanbing Chen ◽  
Qinghua Zhong ◽  
Han Zhang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Specular Reflection ◽  
Disease Diagnosis ◽  
Adaptive Threshold ◽  
The Other ◽  
Imaging Systems ◽  
Color Distribution ◽  
Endoscopic Images ◽  
Highlight Detection

Endoscopic imaging systems have been widely used in disease diagnosis and minimally invasive surgery. Practically, specular reflection (a.k.a. highlight) always exists in endoscopic images and significantly affects surgeons’ observation and judgment. Motivated by the fact that the values of the red channel in nonhighlight area of endoscopic images are higher than those of the green and blue ones, this paper proposes an adaptive specular highlight detection method for endoscopic images. Specifically, for each pixel, we design a criterion for specular highlight detection based on the ratio of the red channel to both the green and blue channels. With the designed criteria, we take advantage of image segmentation and then develop an adaptive threshold with respect to the differences between the red channel and the other ones of neighboring pixels. To validate the proposed method, we conduct experiments on clinical data and CVC-ClinicSpec open database. The experimental results demonstrate that the proposed method yields an averaged Precision, Accuracy, and F1-score rate of 88.76%, 99.60% and 72.56%, respectively, and outperforms the state-of-the-art approaches based on color distribution reported for endoscopic highlight detection.

scholarly journals Voxelisation Algorithms and Data Structures: A Review

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8241
Author(s):  
Mitko Aleksandrov ◽  
Sisi Zlatanova ◽  
David J. Heslop
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
State Of The Art ◽  
Algorithms And Data Structures ◽  
3D Objects ◽  
Digital Representations ◽  
Memory Footprint ◽  
Voxel Data ◽  
2D And 3D ◽  
Geometric Primitives

Voxel-based data structures, algorithms, frameworks, and interfaces have been used in computer graphics and many other applications for decades. There is a general necessity to seek adequate digital representations, such as voxels, that would secure unified data structures, multi-resolution options, robust validation procedures and flexible algorithms for different 3D tasks. In this review, we evaluate the most common properties and algorithms for voxelisation of 2D and 3D objects. Thus, many voxelisation algorithms and their characteristics are presented targeting points, lines, triangles, surfaces and solids as geometric primitives. For lines, we identify three groups of algorithms, where the first two achieve different voxelisation connectivity, while the third one presents voxelisation of curves. We can say that surface voxelisation is a more desired voxelisation type compared to solid voxelisation, as it can be achieved faster and requires less memory if voxels are stored in a sparse way. At the same time, we evaluate in the paper the available voxel data structures. We split all data structures into static and dynamic grids considering the frequency to update a data structure. Static grids are dominated by SVO-based data structures focusing on memory footprint reduction and attributes preservation, where SVDAG and SSVDAG are the most advanced methods. The state-of-the-art dynamic voxel data structure is NanoVDB which is superior to the rest in terms of speed as well as support for out-of-core processing and data management, which is the key to handling large dynamically changing scenes. Overall, we can say that this is the first review evaluating the available voxelisation algorithms for different geometric primitives as well as voxel data structures.

scholarly journals Different dimensions: Intermolecular Associations in 2D and 3D, University of Nottingham; 19–20 June 2003

2003 ◽  
Vol 25 (5) ◽  
pp. 33-34
Author(s):  
Stephen E. Harding ◽  
Paul O'Shea
Keyword(s):  
Molecular Interactions ◽  
Biological Science ◽  
Joint Meeting ◽  
Aqueous Systems ◽  
Membrane Systems ◽  
Biophysical Society ◽  
Different Dimensions ◽  
The University ◽  
2D And 3D

On 19 and 20 June 2003, a joint meeting was held at the University of Nottingham between the Biochemical Society (as part of their Focused Meetings series) and the British Biophysical Society, which focused on the molecular interactions. Interactions between molecules underpin the whole of biological science, both in 2D, as in membrane systems, and in ‘3D’, or aqueous systems.

Sign in / Sign up

Export Citation Format

Share Document