Abstract

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Highlights

  • Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture

  • In medical imaging the interest in deep learning is mostly triggered by convolutional neural networks (CNNs) [56],14 a powerful way to learn useful representations of images and other structured data

  • From acquisition to image retrieval, from segmentation to disease prediction. We divide this into two parts: (i) the signal processing chain close to the physics of MRI, including image restoration and multimodal image registration (Fig. 3), and (ii) the use of deep learning in MR image segmentation, disease detection, disease prediction and systems based on images and text data, addressing a few selected organs such as the brain, the kidney, the prostate and the spine (Fig. 4)

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Summary

Introduction

Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture These are driven by breakthroughs in artificial neural networks, often termed deep learning, a set of techniques and algorithms that enable computers to discover complicated patterns in large data sets. Feeding the breakthroughs is the increased access to data (“big data”), user-friendly software frameworks, and an explosion of the available compute power, enabling the use of neural networks that are deeper than ever before These models nowadays form the state-of-the-art approach to a wide variety of problems in computer vision, language modeling and robotics. We will only mention some bare essentials of the field, hoping that these will serve as useful pointers to the areas that are currently the most influential in medical imaging

Artificial neural networks
Deep learning
Building blocks of convolutional neural networks
From image acquisition to image registration
Data acquisition and image reconstruction
Quantitative parameters – QSM and MR fingerprinting
Image super-resolution
Image synthesis
From image segmentation to diagnosis and prediction
Image registration
Image segmentation
Diagnosis and prediction
Content-based image retrieval
Summary
45 Lipton: Machine Learning
Findings
Perspectives and future expectations
Full Text
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