scholarly journals Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Raabid Hussain ◽  
Alain Lalande ◽  
Kibrom Berihu Girum ◽  
Caroline Guigou ◽  
Alexis Bozorg Grayeli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ct Scan ◽  
Inner Ear ◽  
Temporal Bone ◽  
Ct Images ◽  
Patient Specific ◽  
Micro Ct ◽  
Data Set ◽  
3D Vision

AbstractTemporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network

2021 ◽  
Vol 68 ◽  
pp. 102652
Author(s):  
Vahid Asadpour ◽  
Rex A. Parker ◽  
Patrick R. Mayock ◽  
Samuel E. Sampson ◽  
Wansu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Pancreatic Cancer ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Cancer Tumor

scholarly journals Micro-CT imaging of Thiel-embalmed and iodine-stained human temporal bone for 3D modeling

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
Sebastian Halm ◽  
David Haberthür ◽  
Elisabeth Eppler ◽  
Valentin Djonov ◽  
Andreas Arnold
Keyword(s):  
Soft Tissue ◽  
Temporal Bone ◽  
Middle Ear ◽  
3D Modeling ◽  
Soft Tissues ◽  
Ct Imaging ◽  
Micro Ct ◽  
Data Set ◽  
Iodine Staining ◽  
Tissue Structures

Abstract Introduction This pilot study explores whether a human Thiel-embalmed temporal bone is suitable for generating an accurate and complete data set with micro-computed tomography (micro-CT) and whether solid iodine-staining improves visualization and facilitates segmentation of middle ear structures. Methods A temporal bone was used to verify the accuracy of the imaging by first digitally measuring the stapes on the tomography images and then physically under the microscope after removal from the temporal bone. All measurements were compared with literature values. The contralateral temporal bone was used to evaluate segmentation and three-dimensional (3D) modeling after iodine staining and micro-CT scanning. Results The digital and physical stapes measurements differed by 0.01–0.17 mm or 1–19%, respectively, but correlated well with the literature values. Soft tissue structures were visible in the unstained scan. However, iodine staining increased the contrast-to-noise ratio by a factor of 3.7 on average. The 3D model depicts all ossicles and soft tissue structures in detail, including the chorda tympani, which was not visible in the unstained scan. Conclusions Micro-CT imaging of a Thiel-embalmed temporal bone accurately represented the entire anatomy. Iodine staining considerably increased the contrast of soft tissues, simplified segmentation and enabled detailed 3D modeling of the middle ear.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

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