Deep learning in medical image registration: a survey

By Grant Haskins, Uwe Kruger, Pingkun Yan. Machine Vision and Applications, 2019

682 citations recorded by the source corpus.

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/8e05c28ab022e740a9dc31d5a46e2ad94cac3976

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, neutral): “Usually, a medical technician is supposed to display several images in different directions to reduce the visual contrast between images (Haskins, Kruger, & Yan, 2020).”
Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “See more details in the registration-specific reviews (Ferrante and Paragios 2017; Haskins et al. 2020).”
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “level image processing tasks such as denoising, enhancement, and reconstruction [6], to more high-level image analysis tasks such as segmentation, detection, classification, and registration [7], [8], and even more challenging tasks such as discovering links between the content of medical images and”
A review of multimodal image matching: Methods and applications (2021, background): “– The deep learning framework has shown great potential in MMIM problem, but it still faces several challenges as introduced in [2, 28].”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “There are many survey papers on DL-based key technologies for medical image analysis [22]–[31].”
Transformers in Medical Imaging: A Survey (2022, background): “In medical imaging, registration may be necessary when analyzing a pair of images acquired at different times, from different viewpoints, or using different modalities (like MRI and CT) [75].”
Deep learning in medical image registration: a review (2019, background): “Though several review papers on deep learning in medical image analysis have been published [73, 93, 96, 105, 106, 121, 132, 182], there are very few review papers that are specific to deep learning in medical image registration [60].”
Recent advances and clinical applications of deep learning in medical image analysis (2021, methodology): “…, deep learning models learn a metric that quantifies the similarity between a target/moving image and a reference/fixed image; then the learned similarity metric is used in conjunction with classical (i.e., non-learning-based) frameworks to perform image registration (Haskins et al., 2020).”