Simulation and Synthesis in Medical Imaging

By Alejandro F Frangi, Sotirios A. Tsaftaris, Jerry L Prince. 2018

168 citations recorded by the source corpus.

This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published in this issue covering both mechanistic (5) and phenomenological (7) medical image generation. This rich selection of papers covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology. It also covers all mainstream diagnostic medical imaging modalities. We conclude the editorial with a personal view on the field and highlight some existing challenges and future research opportunities.

Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. P. Heng
2. Qi-Yan Dou
3. Jing Qin
4. Hao Chen
5. Cheng Chen
6. Julien Cohen-Adad
7. Kumar Abhishek
8. Arvid Lundervold

In Biophysics

1. Arvid Lundervold
2. Jing Qin
3. Paul S. Babyn
4. P. Heng
5. Xin Yi
6. Ekta Walia
7. Qi-Yan Dou
8. Hao Chen

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Qi-Yan Dou
3. Hao Chen
4. Jing Qin
5. Cheng Chen
6. Julien Cohen-Adad
7. Arvid Lundervold
8. Paul S. Babyn

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “GANs [79] A generative adversarial network consists of two neural networks pitted against each other.”
• Generative Adversarial Network in Medical Imaging: A Review (2018, methodology): “The basic pix2pix framework has been used for low dose CT denoising (Wolterink et al., 2017b), MR reconstruction (Chen et al., 2018b; Kim et al., 2018; Dar et al., 2018b; Shitrit and Raviv, 2017), and PET denoising (Wang et al., 2018b).”
• Deep semantic segmentation of natural and medical images: a review (2019, background): “Abhishek and Hamarneh (2019) trained a conditional GAN to generate skin lesion images from and confined to binary masks, and showed that using the synthesized images led to a higher skin lesion segmentation accuracy.”
• Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks (2018, background): “Medical image simulation and synthesis have been studied for a while and are increasingly getting traction in medical imaging community [7].”
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (2020, background): “To alleviate the burden of data annotation, some works focus on cross-modality image synthesis so that the segmentation of multiple modalities can be achieved with synthesized images and one-modality labels [51]–[53].”
• A review of medical image data augmentation techniques for deep learning applications (2021, background): “Momeni et al.(175) generated synthetic cerebral microbleeds from MRI by simulating shape variation using Gaussian functions.”