An automated calibration system for in vivo neural network study

2011 ◽  
Author(s):  
Thoa Nguyen ◽  
Carmen Bartic ◽  
Wolfgang Eberle ◽  
Georges Gielen
Keyword(s):  
Neural Network ◽  
Calibration System ◽  
Automated Calibration

scholarly journals Machine learning estimation of tissue optical properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brett H. Hokr ◽  
Joel N. Bixler
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Biological Tissues ◽  
Homogeneous Layer ◽  
In Vivo Measurement ◽  
Tissue Optical Properties ◽  
The Neural Network ◽  
Spatio Temporal ◽  
Fully Connected

AbstractDynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.

scholarly journals Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

2021 ◽  
Author(s):  
Leonardo Tanzi ◽  
Pietro Piazzolla ◽  
Francesco Porpiglia ◽  
Enrico Vezzetti
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Augmented Reality ◽  
Ad Hoc ◽  
Geodesic Distance ◽  
Semantic Segmentation ◽  
Endoscopic Image ◽  
Operative Surgery ◽  
Endoscopic Videos

Abstract Purpose The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient’s organ with its 2D endoscopic image, to assist surgeons during the procedure. Methods This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team’s specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances. Results U-Net stood out as the most effecting architectures for segmentation. ResNet and MobileNet obtained similar Intersection over Unit (IoU) results but MobileNet was able to elaborate almost twice operations per seconds. This segmentation technique outperformed the results from the former work, obtaining an average IoU for the catheter of 0.894 (σ = 0.076) compared to 0.339 (σ = 0.195). This modifications lead to an improvement also in the 3D overlay performances, in particular in the Euclidean Distance between the predicted and actual model’s anchor point, from 12.569 (σ= 4.456) to 4.160 (σ = 1.448) and in the Geodesic Distance between the predicted and actual model’s rotations, from 0.266 (σ = 0.131) to 0.169 (σ = 0.073). Conclusion This work is a further step through the adoption of DL and AR in the surgery domain. In future works, we will overcome the limits of this approach and finally improve every step of the surgical procedure.

An Interlocking Ligamentous Spinal Disk Arthroplasty with Neural Network Infrastructure

2010 ◽  
Vol 7 ◽  
pp. 55-79 ◽  
Author(s):  
Philip Boughton ◽  
James Merhebi ◽  
C. Kim ◽  
G. Roger ◽  
Ashish D. Diwan ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Axial Compression ◽  
Wire Array ◽  
Axial Strain ◽  
Compressive Strain ◽  
Niti Wire ◽  
Motion Segment ◽  
Flexion Extension

An elastomeric spinal disk prosthesis design (BioFI™) with vertebral interlocking anchors has been modified using an embedded TiNi wire array. Bioinert styrenic block copolymer (Kraton®) and polycarbonate urethane (Bionate®) thermoplastic elastomer (TPE) matrices were utilized. Fatigue resistant NiTi wire was pretreated to induce superelastic martensitic microstructure. Stent-like helical structures were produced for incorporation within homogenous TPE matrix. Composite prototypes were fabricated in a vacuum hot press using transfer moulding techniques. Implant prototypes were subject to axial compression using a BOSE ® ELF3400. The NiTi reinforced implants exhibited reduction in axial strain, compliance, and creep compared to TPE controls. The axial properties of the NiTi reinforced Bionate® BioFI™ implant best approximated those of a spinal disk followed by Kraton®-NiTi, Bionate® and Kraton® prototypes. An ovine lumbar segment biomechanical model was used to characterize the disk prosthesis prototypes. Specimens were subject to 7.5Nm pure moments in axial rotation, flexion-extension and lateral bending with a custom jig mounted on an Instron® 8874. The motion preserving ligamentous nature of this arthroplasty prototype was not inhibited by NiTi reinforcement. Joint stiffness for all prototypes was significantly less than the intact and discectomy controls. This was due to lack of vertebral anchor rigidity rather than BioFI™ motion segment matrix type or reinforcement. Implant stress profiles for axial compression and axial torsion conditions were obtained using finite element methods. The biomechanical testing and finite element modelling both support existing BioFI™ design specifications for higher modulus vertebral anchors, endplates and motion segment periphery with gradation to a low modulus core within the motion segment. This closer approximation of the native spinal disk form translates to improvements in prosthesis biomechanical fidelity and longevity. Axial compressive strain induced within a TiNi reinforced Kraton® BioFI™ was found to be linearly proportional to the NiTi helical coil electrical resistance. This neural network capability delivers opportunities to monitor and telemeterize in situ multiaxis joint structural performance and in vivo spine biomechanics.

Study on Intelligent Controller Design of Flow Metrological Calibration System

2020 ◽  
Author(s):  
Xiong Yin ◽  
Kai Wen ◽  
Yan Wu ◽  
Lei Zhou ◽  
Jing Gong
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Pid Controller ◽  
Controller Design ◽  
Calibration System ◽  
Calibration Process ◽  
Intelligent Controller ◽  
Flow Adjustment ◽  
Precise Adjustment ◽  
Valve Opening

Abstract In recent years, China ramped up imports of natural gas to satisfy the growing demand, which has increased the number of trade meters. Natural gas flowmeters need to be calibrated regularly at calibration stations to ensure their accuracy. Nowadays, the flow metrological calibration process is done by the operator manually in China, which is easy to be affected by personnel experience and proficiency. China is vigorously developing industry 4.0 and AI(artificial intelligence) technologies. In order to improve the calibration efficiency, a design scheme of intelligent controller for flow metrological calibration system is first proposed in this paper. The intelligent controller can replace the operator for process switching and flow adjustment. First, the controller selects the standard flowmeter according to the type of the calibrated flowmeter, and switches the calibration process. To accurately control the calibration flow for 180 seconds, the controller continuously adjusts the regulating valve with a sequence of commands to the actuator. These commands are generated by intelligent algorithm which is predefined in the controller. Process switching is operated automatically according to flowmeter calibration specifications. In order to reach the required flow point quickly, the flow adjustment is divided into two steps: preliminary adjustment and precise adjustment. For preliminary adjustment, a BP neural network will be built first using the field historical data and simulation results. This neural network describes the relationship between the valve-opening scheme and the calibration flow. Therefore, it could give a calibration flow as close as possible to the expected value during calibration. For precise adjustment, an adaptive PID controller is used. It could adjust the valve opening degree automatically to make sure the flow deviation meet the calibration requirements. Since the PID controller is a self-adaptive PID controller, the process of adjustment is very quick, which can reduce the calibration time largely. After each calibration, both the original neural network and the adaptive function of the controller will be updated to achieve the self-growth. With the information of the calibrated flowmeter, the entire calibration system can run automatically. The experiment in a calibration station shows that the intelligent controller can control the deviation of the flow value within 5% during 4∼5 minutes.

An integrated and automated calibration system for pneumatic piston gauges

Measurement ◽  
2019 ◽  
Vol 134 ◽  
pp. 1-5 ◽  
Author(s):  
Yuanchao Yang ◽  
R. Gregory Driver ◽  
John S. Quintavalle ◽  
Julia Scherschligt ◽  
Katie Schlatter ◽  
...  
Keyword(s):  
Calibration System ◽  
Automated Calibration

scholarly journals Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

2020 ◽  
Author(s):  
Yajun Liu ◽  
Yilin Guo ◽  
Ya Gao ◽  
Guiming Hu ◽  
Ju Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Human Placenta ◽  
Chorionic Villus ◽  
Gene Interaction ◽  
Gene Interaction Networks ◽  
Transcriptional Regulatory ◽  
High Resolution Images

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

scholarly journals Homeostatic plasticity and external input shape neural network dynamics

2018 ◽  
Author(s):  
Johannes Zierenberg ◽  
Jens Wilting ◽  
Viola Priesemann
Keyword(s):  
Neural Network ◽  
Brain Area ◽  
External Input ◽  
Homeostatic Plasticity ◽  
Dynamic State ◽  
Network Topologies ◽  
Neural Network Dynamics ◽  
Spiking Activity

In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.

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