Metal Artifacts Reduction in CT Scans using Convolutional Neural Network with Ground Truth Elimination

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

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Geometric property-based convolutional neural network for indoor object detection

International Journal of Advanced Robotic Systems ◽

10.1177/1729881421993323 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142199332

Author(s):

Xintao Ding ◽

Boquan Li ◽

Jinbao Wang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Geometric Property ◽

Ground Truth ◽

Geometric Constraints ◽

Depth Information ◽

Training Set ◽

Object Knowledge ◽

The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

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Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

Communications Biology ◽

10.1038/s42003-021-01697-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Peter M. Maloca ◽

Philipp L. Müller ◽

Aaron Y. Lee ◽

Adnan Tufail ◽

Konstantinos Balaskas ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Optical Coherence Tomography ◽

Image Segmentation ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Ground Truth ◽

Optical Coherence Tomography Image ◽

Optical Coherence ◽

Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

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Artifacts Reduction for Compression Image with Pyramid Residual Convolutional Neural Network

Proceedings of the 3rd International Conference on Video and Image Processing ◽

10.1145/3376067.3376086 ◽

2019 ◽

Author(s):

Chunmei Nian ◽

Ruidong Fang ◽

Jucai Lin ◽

Zhengteng Zhang ◽

Jun Yin ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Artifacts Reduction

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AI INFLUENCE IN COVID-19 DETECTION

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/07235 ◽

2021 ◽

Vol 23 (07) ◽

pp. 1116-1120

Author(s):

Cijil Benny ◽

Keyword(s):

Neural Network ◽

Statistical Analysis ◽

Convolutional Neural Network ◽

Case Reports ◽

Ct Scans ◽

Data Sets ◽

X Rays ◽

Medical Field ◽

Primary Key ◽

Made In

This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point. The best-analyzing methods on the studies were Susceptible Infected Recovered and Susceptible Exposed Infected Removed respectively. Whereas the implementation of AI is mostly done in X-rays and CT- Scans with the help of a Convolutional Neural Network. To accomplish the paper several data sets are used. They include medical and case reports, medical strategies, and persons respectively. Approaches are being done through shared statistical analysis based on these reports. Considerably the acceptance COVID is being shared and it is also reachable. Furthermore, much regulation is needed for handling this pandemic since it is a threat to global society. And many more discoveries shall be made in the medical field that uses AI as a primary key source.

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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

Acta Neurochirurgica ◽

10.1007/s00701-020-04483-7 ◽

2020 ◽

Vol 162 (12) ◽

pp. 3067-3080

Author(s):

Yizhou Wan ◽

Roushanak Rahmat ◽

Stephen J. Price

Keyword(s):

Neural Network ◽

Overall Survival ◽

Convolutional Neural Network ◽

Multiple Testing ◽

Network Performance ◽

Cox Regression ◽

Prognostic Significance ◽

Ground Truth ◽

Cox Regression Analysis ◽

Volumetric Features

Abstract Background Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.

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Convolutional neural network for the detection of pancreatic cancer on CT scans – Authors' reply

The Lancet Digital Health ◽

10.1016/s2589-7500(20)30188-6 ◽

2020 ◽

Vol 2 (9) ◽

pp. e454

Author(s):

Wei-Chih Liao ◽

Amber L Simpson ◽

Weichung Wang

Keyword(s):

Neural Network ◽

Pancreatic Cancer ◽

Convolutional Neural Network ◽

Ct Scans

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Segmentation and Measurements of Carotid Intima-Media Thickness in Ultrasound Images Using the Improved Convolutional Neural Network and Support Vector Machine

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.326415 ◽

2021 ◽

Vol 11 (1) ◽

pp. 15-24

Author(s):

Dequan Guo ◽

Gexiang Zhang ◽

Hui Peng ◽

Jianying Yuan ◽

Prithwineel Paul ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Intima Media Thickness ◽

Ground Truth ◽

Classification Model ◽

Support Vector ◽

Ultrasound Images ◽

Boundary Points ◽

Media Thickness

In recent years, diseases of cardiovascular and cerebrovascular have attracted much attention due to main causes in death in human beings. To reduce mortality, there are lots of efforts which are focused on early diagnosis and prevention. It is an important reference index for cardiovascular diseases through the endovascular membrane in carotid artery by medical ultrasound images. The paper proposes a method which finds the region of interest (ROI) by convolutional neural network, segments and measures intima-media membrane mainly using support vector machine (SVM). Essentially, the task of detecting the membrane is one target detection problem. This paper adopts the strategy, named Yon Only Look Once (YOLO), a new detection algorithm, and follows the convolution neural network algorithm based on end-to-end training. Firstly, sufficient samples are extracted according to certain characteristics in the special region. It can be trained by the SVM classification model. Then the ROI is processed and all the pixels are classified into boundary points and non-boundary points through the classification model. Thirdly, the boundary points are selected to obtain the accurate boundary and calculate the intima-media thickness (IMT). In experiments, two hundred ultrasound images are tested, and the results verify that our algorithm is consistent with the results by ground truth (GT). The detection speed of the algorithm in this paper is in real time, and it has high generalization characteristics. The algorithm computes the intima-media thickness in ultrasound images accurately and quickly with 95% consistence to ground truth.

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