An overview of deep learning in medical imaging focusing on MRI (1811.10052v2)

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 machine 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.

An Overview of Deep Learning in Medical Imaging Focusing on MRI

The use of deep learning in medical imaging has seen a significant increase in interest and application, particularly within the domain of Magnetic Resonance Imaging (MRI). The paper "An overview of deep learning in medical imaging focusing on MRI" by Alexander Selvikvåg Lundervold and Arvid Lundervold aims to delineate the advances and challenges associated with integrating deep learning techniques into MRI processes, from image acquisition to disease prediction. The paper provides a broad yet detailed examination of how deep learning is transforming several critical aspects of MRI.

Introduction to Deep Learning

The paper begins with a concise overview of deep learning, a subfield of machine learning that utilizes artificial neural networks (ANNs) to enable intricate pattern recognition in large datasets. In specific contexts such as medical imaging, convolutional neural networks (CNNs) are widely used due to their proficiency in handling image data by recognizing spatial hierarchies through convolutions. The discussion also touches upon the history and evolution of neural networks, pinpointing the pivotal role of hardware advancements and user-friendly deep learning frameworks in the proliferation of these techniques.

Applications Along the MRI Workflow

Deep learning applications in MRI span the entire MR workflow:

1. Image Acquisition and Reconstruction: CNNs and RNNs have been pivotal in reconstructing high-quality MR images from undersampled data. Methods such as convolutional recurrent neural networks have demonstrated superior performance in dynamic MRI reconstructions. Techniques like variational networks and GANs have further advanced MRI reconstruction by reducing noise and improving the realism of synthesized images.
2. Quantitative Imaging: In fields like quantitative susceptibility mapping (QSM) and magnetic resonance fingerprinting (MRF), deep learning has been used to expedite and enhance the estimation of quantitative tissue properties. For instance, CNNs have been effectively used to perform the field-to-source inversion in QSM, while deep neural networks have been employed to accurately infer tissue parameters from signal evolutions in MRF.
3. Image Restoration: Autoencoders and other deep learning models improve signal-to-noise ratios in MRI by denoising images. Advanced models customized for spatio-temporal data have shown substantial advantages in dynamic contrast-enhanced (DCE) MRI by reducing noise while preserving critical dynamic features important for diagnosis.
4. Super-Resolution: Super-resolution techniques amplify MRI resolution by constructing higher-resolution images from low-resolution inputs. Approaches using CNNs have been successful in super-resolving multiple MR modalities, thus enhancing diagnostic utilities without the burden of extensive scan times.
5. Image Synthesis: GANs have enabled the generation of synthetic MRI with attributes resembling actual clinical images, which is essential for augmenting training datasets or anonymizing patient data. These implementations can also be extended for creating completely new image contrasts from existing modalities.
6. Image Registration: Deep learning has significantly modernized image registration processes, making them faster and more accurate. These techniques have shown promise in deformable registration and alignment of multimodal image data, crucial for longitudinal studies and integrating multispectral scans.

Segmentation, Diagnosis, and Prediction

Deep learning's impact is also profound in the higher-level tasks of image segmentation and disease prediction:

• Segmentation: The segmentation of anatomical structures in MRI is quintessential for diagnosis and treatment planning. CNN architectures, especially U-Net and variants, have set new benchmarks in segmenting brain tissues, tumors, kidneys, prostates, and other critical organs.
• Disease Prediction: Deep learning models are instrumental in predicting diseases such as Alzheimer's and various cancers by learning complex patterns across large datasets. Transfer learning and multimodal inputs integrating MRI with other data sources (e.g., PET scans) have further refined predictive accuracies in these domains.

Content-Based Image Retrieval

Content-based image retrieval (CBIR) systems assist radiologists by retrieving similar medical images from large databases, thereby facilitating comparative diagnosis and decision making. Advanced deep learning models make use of multi-layered image encodings to efficiently match and retrieve relevant cases.

Open Science and Reproducible Research

The authors emphasize the importance of open science and reproducibility in the fast-paced field of machine learning. They provide examples of open-source implementations and data repositories which have proven instrumental in advancing research by ensuring transparency and accessibility.

Implications and Future Directions

The integration of deep learning into MRI workflows holds tremendous potential for revolutionizing medical imaging. Practically, it offers substantial improvements in image quality, diagnostic accuracy, and computational efficiency. Theoretically, it paves the way for new paradigms in quantitative imaging and automated diagnosis. However, challenges related to data privacy, interpretability, and regulatory compliance still need to be meticulously addressed. The future likely involves more robust systems reliant on federated learning, techniques for comprehensive uncertainty quantification, and a deeper convergence of machine learning with clinical practices.

In conclusion, while deep learning continues to shape the landscape of medical imaging, particularly MRI, ongoing advancements and adherence to rigorous standards of research practice will determine the extent and sustainability of its clinical impact.