TransMorph: Transformer for unsupervised medical image registration

Chen, Junyu; Frey, Eric; He, Yufan; Segars, W. Paul; Li, Ye; Du, Yong

doi:10.1016/j.media.2022.102615

Cited by 255 publications

(97 citation statements)

References 57 publications

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“…Chen et al. ( 149 ) designed a ViT-V-Net for brain MRI image registration. They adopted a hybrid ConvNet-and-Transformer architecture to apply ViT for high-level feature learning.…”

Section: Methodsmentioning

confidence: 99%

A review of deep learning-based deformable medical image registration

et al. 2022

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The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

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“…Chen et al. ( 149 ) designed a ViT-V-Net for brain MRI image registration. They adopted a hybrid ConvNet-and-Transformer architecture to apply ViT for high-level feature learning.…”

Section: Methodsmentioning

confidence: 99%

A review of deep learning-based deformable medical image registration

et al. 2022

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“…Some researchers placed emphasis on how to integrate the Transformer with the U‐Net to boost the performance of registration, including 225,227,229,232 . Wang et al 225 proposed to join the Transformer with the U‐Net structure for the medical image registration.…”

Section: Medical Image Registrationmentioning

confidence: 99%

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations.In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

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“…and multitime (pre‐ and post‐therapy) images may be beneficial to improve model performance 27,28,43,47 . Several efforts have been made in‐depth on data complementation and registration algorithms 73,74 . However, how to make good use of multimodal and multitime images remains challenging.…”

Section: Challenges and Future Of Aimentioning

confidence: 99%

“…27,28,43,47 Several efforts have been made in-depth on data complementation and registration algorithms. 73,74 However, how to make good use of multimodal and multitime images remains challenging.…”

Section: Challenges and Future Of Aimentioning

confidence: 99%

MRI‐Based Artificial Intelligence in Rectal Cancer

Wong

et al. 2022

Magnetic Resonance Imaging

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Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future.

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TransMorph: Transformer for unsupervised medical image registration

Cited by 255 publications

References 57 publications

A review of deep learning-based deformable medical image registration

A review of deep learning-based deformable medical image registration

Recent advances of Transformers in medical image analysis: A comprehensive review

MRI‐Based Artificial Intelligence in Rectal Cancer

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