A review on medical imaging synthesis using deep learning and its clinical applications

Wang, Tonghe; Lei, Yang; Fu, Yabo; Wynne, Jacob; Curran, Walter J.; Tian, Lei; Yang, Xiaofeng

doi:10.1002/acm2.13121

Cited by 192 publications

(145 citation statements)

References 183 publications

(334 reference statements)

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“…In addition, RFs keep achieving very promising results in recent applications related to outcome prediction [135,[139][140][141][142][143], but also for other domains like image classification [113,144] or automatic treatment planning [100,[145][146][147]. Regarding other tasks where RFs were among the state-of-the-art methods a few years ago, like image synthesis [148][149][150] or segmentation [151,152], the community has now fully switched the attention to CNNs [5,153,154]. Nevertheless, in favor of RFs one could argue that they are easy to implement and less computationally expensive than CNNs (i.e., they can work in regular CPU).…”

Section: Random Forests (Rfs)mentioning

confidence: 99%

“…For certain applications, such as image segmentation [154,157,158] or synthesis [5], CNNs are now considered the state-of-the-art methods [4]. Although the comparison of different algorithms on the same dataset is not so common, an excellent way to track the evolution of the state-of-the-art algorithms is to look at the challenges and competitions organised around specific topics.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…The medical community has taken advantage of these extraordinary developments in order to build AI applications that get the most of medical images, automating different steps of the clinical practice or providing support for clinical decisions. Papers relying on AI and ML report promising results in a wide range of medical applications [1][2][3][4][5][6][7]. Disease diagnosis, image segmentation or outcome prediction are some of the tasks that are experiencing a disruptive transformation thanks to the latest progress of AI.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

200

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Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

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Section: Random Forests (Rfs)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

200

View full text Add to dashboard Cite

show abstract

“…Inspired by the tremendous success of deep learning in computer vision, 24–27 deep learning‐based methods have been recently investigated for medical image reconstruction, 28–31 analysis, 32–36 and synthesis 37,38 . Studies have demonstrated deep learning‐based approaches significantly outperform over CS‐based methods for image reconstruction 39‐42 .…”

Section: Introductionmentioning

confidence: 99%

Self‐supervised learning for accelerated 3D high‐resolution ultrasound imaging

et al. 2021

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Purpose Ultrasound (US) imaging has been widely used in diagnosis, image‐guided intervention, and therapy, where high‐quality three‐dimensional (3D) images are highly desired from sparsely acquired two‐dimensional (2D) images. This study aims to develop a deep learning‐based algorithm to reconstruct high‐resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images. Methods We propose a self‐supervised learning framework using cycle‐consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low‐resolution (LR) US images, respectively. The two sets of LR US images are obtained through down‐sampling the original US images along the two axes, respectively. In US imaging, in‐plane spatial resolution is generally much higher than through‐plane resolution. By learning the mapping from down‐sampled in‐plane LR images to original HR US images, cycleGAN can generate through‐plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models. Results The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90 ± 0.15, the peak signal‐to‐noise ratio (PSNR) value of 37.88 ± 0.88 dB, and the visual information fidelity (VIF) value of 0.69 ± 0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases. Conclusions We have proposed and investigated a new deep learning‐based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through‐plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self‐supervision capability could accelerate HR US imaging.

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“…Recently, Krämer et al proposed a fully automated segmentation algorithm using dual-echo, UTE MRI data (18). Machine learning-based and deep learningbased segmentation and synthetic CT (also known as pseudo CT) generation methods have been intensively studied for the last decades (19)(20)(21)(22), among which random forest-based method is one popular machine learning approach. The popularity of random forest arises from its appealing features, such as its capability of handling a large variety of features and enabling feature sharing of a multi-class classifier, robustness to noise and efficient parallel processing (23).…”

Section: Introductionmentioning

confidence: 99%

MRI classification using semantic random forest with auto-context model

Lei¹,

Wang²,

Deng³

et al. 2021

Quant Imaging Med Surg

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Background: It is challenging to differentiate air and bone on MR images of conventional sequences due to their low contrast. We propose to combine semantic feature extraction under auto-context manner into random forest to improve reasonability of the MRI segmentation for MRI-based radiotherapy treatment planning or PET attention correction.Methods: We applied a semantic classification random forest (SCRF) method which consists of a training stage and a segmentation stage. In the training stage, patch-based MRI features were extracted from registered MRI-CT training images, and the most informative elements were selected via feature selection to train an initial random forest. The rest sequence of random forests was trained by a combination of MRI feature and semantic feature under an auto-context manner. During segmentation, the MRI patches were first fed into these random forests to derive patch-based segmentation. By using patch fusion, the final endto-end segmentation was obtained.Results: The Dice similarity coefficient (DSC) for air, bone and soft tissue classes obtained via proposed method were 0.976±0.007, 0.819±0.050 and 0.932±0.031, compared to 0.916±0.099, 0.673±0.151 and 0.830±0.083 with random forest (RF), and 0.942±0.086, 0.791±0.046 and 0.917±0.033 with U-Net. SCRF also outperformed the competing methods in sensitivity and specificity for all three structure types. Conclusions:The proposed method accurately segmented bone, air and soft tissue. It is promising in facilitating advanced MR application in diagnosis and therapy.

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A review on medical imaging synthesis using deep learning and its clinical applications

Cited by 192 publications

References 183 publications

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence and machine learning for medical imaging: A technology review

Self‐supervised learning for accelerated 3D high‐resolution ultrasound imaging

MRI classification using semantic random forest with auto-context model

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