Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort

Frueh, Marcel; Küstner, Thomas; Nachbar, Marcel; Thorwarth, Daniela; Schilling, Andreas; Gatidis, Sergios

doi:10.1016/j.cmpb.2022.107085

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…14,15 Deep learning methods in real-time and 4D MRI overcome MRI's long acquisition time and the low field strengths of the MRI-LINAC by reconstructing images from undersampled k-space, 16 synthesizing additional MRI slices, 17 and exploiting periodic motion to improve image quality. 18 In this review, we systematically examine studies that apply deep learning to MRgRT, categorizing them based on their application and highlighting interesting or important contributions. We identify four distinct areas of deep learning methods which enhance the clinical workflow: segmentation, synthesis, radiomics (classification), and real-time/4D MRI.…”

Section: Introductionmentioning

confidence: 99%

“…Radiomics represents an eclectic body of works but can be divided into studies which classify structures in an MRI image 13 or prognostic models which use MR images to predict treatment outcomes such as tumor recurrence or adverse effects 14,15 . Deep learning methods in real‐time and 4D MRI overcome MRI's long acquisition time and the low field strengths of the MRI‐LINAC by reconstructing images from undersampled k‐space, 16 synthesizing additional MRI slices, 17 and exploiting periodic motion to improve image quality 18 …”

Section: Introductionmentioning

confidence: 99%

“… 14 , 15 Deep learning methods in real‐time and 4D MRI overcome MRI's long acquisition time and the low field strengths of the MRI‐LINAC by reconstructing images from undersampled k‐space, 16 synthesizing additional MRI slices, 17 and exploiting periodic motion to improve image quality. 18 …”

Section: Introductionmentioning

confidence: 99%

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Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x‐ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi‐modal, visual transformer, and diffusion models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning in MRI‐guided radiation therapy: A systematic review

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Research on CBRN Practical Assessment Technology Based on Artificial Intelligence Technology

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On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

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et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort

Cited by 9 publications

References 23 publications

Deep learning in MRI‐guided radiation therapy: A systematic review

Deep learning in MRI‐guided radiation therapy: A systematic review

Research on CBRN Practical Assessment Technology Based on Artificial Intelligence Technology

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

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