Medical image analysis is currently experiencing a paradigm shift due to deep learning. This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in “Medical Imaging with Deep Learning” in the year 2018. This paper surveys the recent developments in this direction and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy. This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. This enables us to single out “lack of appropriately annotated large-scale data sets” as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging Community to fully harness deep learning in the future.

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

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List of Deep Learning Models

This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published in this issue covering both mechanistic (5) and phenomenological (7) medical image generation. This rich selection of papers covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology. It also covers all mainstream diagnostic medical imaging modalities. We conclude the editorial with a personal view on the field and highlight some existing challenges and future research opportunities.

Simulation and Synthesis in Medical Imaging

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

The detection and subsequent correction of motion artifacts is essential for the high diagnostic value of non- invasive coronary angiography using cardiac CT. However, motion correction algorithms have a substantial computational footprint and possible failure modes which warrants a motion artifact detection step to decide whether motion correction is required in the first place. We investigate how accurately motion artifacts in the coronary arteries can be predicted by deep learning approaches. A forward model simulating cardiac motion by creating and integrating artificial motion vector fields in the filtered back projection (FBP) algorithm allows us to generate training data from nine prospectively ECG-triggered high quality clinical cases. We train a Convolutional Neural Network (CNN) classifying 2D motion-free and motion-perturbed coronary cross-section images and achieve a classification accuracy of 94:4% ± 2:9% by four-fold cross-validation.

Deep-learning-based CT motion artifact recognition in coronary arteries

Coronary CT angiography has become a preferred technique for the detection and diagnosis of coronary artery disease, but image artifacts due to cardiac motion frequently interfere with evaluation. Several motion compensation approaches have been developed which deal with motion estimation based on 3-D/3-D registration of multiple heart phases. The scan range required for multi-phase reconstruction is a limitation in clinical practice. In this paper, the feasibility of single-phase, image-based motion estimation by convolutional neural networks (CNNs) is investigated. First, the required data for supervised learning is generated by a forward model which introduces simulated axial motion to artifact-free CT cases. Second, regression networks are trained to estimate underlying 2D motion vectors from axial coronary cross-sections. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion strength with average accuracies of 1.08◦ and 0.06 mm, respectively. Motivated by these results, clinical performance is evaluated based on twelve prospectively ECG-triggered clinical cases and achieves average accuracies of 20.66◦ and 0.94 mm. Transferability and generalization capabilities are demonstrated by motion estimation and subsequent compensation on six clinical cases with real cardiac motion artifacts.

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Tobias Wissel

Papers

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Deep-learning-based CT motion artifact recognition in coronary arteries

Motion Estimation in Coronary CT Angiography Images using Convolutional Neural Networks