scholarly journals Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

2021 ◽  
Author(s):  
Wei Wei ◽  
Xu Haishan ◽  
Marko Rak ◽  
Christian Hansen
Keyword(s):  
Deep Learning ◽  
Average Distance ◽  
Angle Error ◽  
Registration Accuracy ◽  
Clinical Images ◽  
Distance Error ◽  
Patient Registration ◽  
Channel Input ◽  
Coarse Registration ◽  
High Resolution Images

Abstract Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.

scholarly journals A deep learning based approach for trajectory estimation using geographically clustered data

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Aditya Shrivastava ◽  
Jai Prakash V Verma ◽  
Swati Jain ◽  
Sanjay Garg
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Average Distance ◽  
Trajectory Data ◽  
Distance Error ◽  
Large Scale Data ◽  
Route Prediction ◽  
One Step ◽  
Scale Data

AbstractThis study presents a novel approach to predict a complete source to destination trajectory of a vehicle using a partial trajectory query. The proposed architecture is scalable to extremely large-scale data with respect to the dense road network. A deep learning model Long Short Term Memory (LSTM) has been used for analyzing the temporal data and predicting the complete trajectory. To handle a large amount of data, clustering of similar trajectory data is used that helps in reducing the search space. The clusters based on geographical locations and temporal values are used for training different LSTM models. The proposed approach is compared with the other published work on the parameters as Average distance error and one step prediction accuracy The one-step prediction accuracy is as good as 81% and Distance error are .33 Km. Our proposed approach termed Clustered LSTM is outperforming in both the parameters when compared with other reported results. The proposed solution is a clustering-based predictive model that effectively contributes to accurately handle the large scale data. The outcome of this study leads to improvise the navigation systems, route prediction, traffic management, and location-based recommendation systems.

scholarly journals Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

2020 ◽  
Vol 12 (4) ◽  
pp. 739
Author(s):  
Keiller Nogueira ◽  
Gabriel L. S. Machado ◽  
Pedro H. T. Gama ◽  
Caio C. V. da Silva ◽  
Remis Balaniuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Impact ◽  
Automatic Machine ◽  
Feature Representation ◽  
Data Driven ◽  
Maintenance Costs ◽  
Crucial Step ◽  
Machine Learning Methods ◽  
High Resolution Images

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.

scholarly journals Accelerating Super-Resolution and Visual Task Analysis in Medical Images

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Task Analysis ◽  
Multiple Scales ◽  
Medical Images ◽  
Computational Cost ◽  
Super Resolution ◽  
Visual Task ◽  
Learning Networks ◽  
High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

scholarly journals Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 265
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Fei Teng ◽  
Yunhao Lin ◽  
Mengjie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Hunan Province ◽  
Building Roof ◽  
Rural Buildings ◽  
High Resolution Images ◽  
Rgb Images ◽  
Sobel Edge Detection ◽  
Model Recognition

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

scholarly journals SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

Urban Forest Identification from High-Resolution Images Using Deep-Learning Method

2021 ◽  
Author(s):  
Wei Wang ◽  
Rongyuan Liu ◽  
Huiyun Yang ◽  
Ping Zhou ◽  
Xiangwen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Urban Forest ◽  
Learning Method ◽  
High Resolution Images

scholarly journals Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5310
Author(s):  
Lai Kang ◽  
Yingmei Wei ◽  
Jie Jiang ◽  
Yuxiang Xie
Keyword(s):  
Deep Learning ◽  
Image Alignment ◽  
Wide Field ◽  
Camera Motion ◽  
Alignment Method ◽  
Feature Correspondence ◽  
Iterative Optimization ◽  
Scene Representation ◽  
Feature Based ◽  
High Resolution Images

Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments.

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