scholarly journals Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyunkwang Lee ◽  
Chao Huang ◽  
Sehyo Yune ◽  
Shahein H. Tajmir ◽  
Myeongchan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Image Space ◽  
Sensor Data ◽  
Reconstruction Process ◽  
Partial Representation

Abstract Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

scholarly journals 3D Deep Learning on Medical Images: A Review

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5097 ◽  
Author(s):  
Satya P. Singh ◽  
Lipo Wang ◽  
Sukrit Gupta ◽  
Haveesh Goli ◽  
Parasuraman Padmanabhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Imaging Data ◽  
Learning Models ◽  
Processing Technologies ◽  
3D Cnn

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

scholarly journals A Tour of Unsupervised Deep Learning for Medical Image Analysis

2021 ◽  
Vol 17 ◽  
Author(s):  
Khalid Raza ◽  
Nripendra Kumar Singh
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Heterogeneous Data ◽  
Learning Techniques ◽  
Deep Boltzmann Machine ◽  
Unsupervised Deep Learning

Background: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available. Objectives: The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and its other variants, Restricted Boltzmann machines (RBM), Deep belief networks (DBN), Deep Boltzmann machine (DBM), and Generative adversarial network (GAN). Further, future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed. Conclusion: Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

scholarly journals Deep Convolution Neural Networks for Medical Image Analysis

2018 ◽  
Vol 7 (3.33) ◽  
pp. 115 ◽  
Author(s):  
Myung Jae Lim ◽  
Da Eun Kim ◽  
Dong Kun Chung ◽  
Hoon Lim ◽  
Young Man Kwon
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Histopathological Image ◽  
Approach Method

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.  

scholarly journals Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

2021 ◽  
Vol 3 ◽  
Author(s):  
Dan Luo ◽  
Wei Zeng ◽  
Jinlong Chen ◽  
Wei Tang
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Data Sources ◽  
X Rays ◽  
Advantages And Disadvantages ◽  
Backbone Network ◽  
Active Research

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

2021 ◽  
Author(s):  
Judith Herrmann ◽  
Sebastian Gassenmaier ◽  
Thomas Kuestner ◽  
Matthias Kuendel ◽  
Dominik Nickel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Acquisition Time ◽  
Clinical Implementation ◽  
Clinical Workflow ◽  
Diagnostic Confidence ◽  
Clinical Routine ◽  
Time Saving ◽  
Reconstruction Process

Abstract Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainable image quality compared to conventional TSE.Methods: DL image reconstruction using a variational network was clinically implemented to enable acquisition of accelerated TSE sequences. After internal review board’s approval and informed consent, 30 examinations for knee, shoulder, and lumbar spine in 15 volunteers at 3 T were included in this prospective study. Conventional TSE sequences (TSE) and TSE with deep learning reconstruction (TSEDL) were compared regarding overall image quality, noise, sharpness, and subjective signal-to-noise-ratio (SNR), as well diagnostic confidence and image impression. Comparative analyses were conducted to assess the differences between the sequences. A survey on technologists’ acceptance was performed for DL image reconstruction. Results: DL image reconstruction was successfully implemented in a clinical workflow and TSEDL allowed a remarkable time saving of more than 50%. Overall image quality, diagnostic confidence and image impression for TSEDL were rated as excellent (median 4, IQR 4-4) and comparable to TSE (image quality: p=0.059, diagnostic confidence: p=0.157, image impression: p=0.102). Noise, sharpness, artifacts, and subjective SNR for TSEDL reached significantly superior levels to TSE (noise: p<0.001, sharpness: p=0.001, artifacts: p=0.014, subjective SNR: p<0.001). Technologists reported high levels of acceptance for DL image reconstruction. Required time for the reconstruction process was rated moderate and longer than standard sequences (median 2, IQR 2-3). Required time and effort for the implementation in daily workflow was rated as low effort (median 4, IQR 3-4). General applicability of DL reconstruction as well as acceptance of DL sequences in clinical routine were rated excellent (median 4, IQR 3-4). Conclusion: DL image reconstruction for TSE sequences can be implemented in clinical workflow and enables a remarkable time saving (>50%) in image acquisition while maintaining excellent image quality.Trial registration: Your clinical trial is officially registered at the German DRKS with the registration number: DRKS00023278.

Use of deep learning to predict acute kidney injury after intravenous contrast media administration (Preprint)

2021 ◽  
Author(s):  
Donghwan Yun ◽  
Semin Cho ◽  
Yong Chul Kim ◽  
Dong Ki Kim ◽  
Kook-Hwan Oh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Deep Learning ◽  
Contrast Media ◽  
Learning Algorithm ◽  
Kidney Injury ◽  
Time Varying ◽  
Intravenous Contrast ◽  
Deep Learning Algorithm ◽  
Intravenous Contrast Media

BACKGROUND Precise prediction of contrast media-induced acute kidney injury (CIAKI) is an important issue because of its relationship with worse outcomes. OBJECTIVE Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography. METHODS A total of 14,185 cases that underwent intravenous contrast media for computed tomography under the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine ≥0.3 mg/dl within 2 days and/or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine, extreme boosting machine, random forest, decision tree, support vector machine, κ-nearest neighboring, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set. RESULTS CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC value of 0.755 (0.708–0.802) for predicting CIAKI, which was superior to those obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine ≥0.5 mg/dl and/or ≥25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (0.664–0.768). In the feature ranking analysis, albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function. CONCLUSIONS Application of a deep learning algorithm improves the predictability of intravenous CIAKI after computed tomography, representing a basis for future clinical alarming and preventive systems.

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