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Zhou B, Qin B, Zhou Q, Sun D, Chen P, Yang K, Pan Q, Li H. Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:39271-39284. [PMID: 38814555 DOI: 10.1007/s11356-024-33752-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/17/2024] [Indexed: 05/31/2024]
Abstract
To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.
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Affiliation(s)
- Banghao Zhou
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Botao Qin
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China.
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China.
| | - Qun Zhou
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Daowei Sun
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Pengpeng Chen
- School of Computer Science & Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Kai Yang
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Qingyan Pan
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Computer Science & Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Huizhen Li
- Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Ministry of Education, Xuzhou, 221116, Jiangsu, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
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Chen R, Zhang W, Song F, Yu H, Cao D, Zheng Y, He M, Shi D. Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening. NPJ Digit Med 2024; 7:34. [PMID: 38347098 PMCID: PMC10861476 DOI: 10.1038/s41746-024-01018-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 01/18/2024] [Indexed: 02/15/2024] Open
Abstract
Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79-0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage.
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Affiliation(s)
- Ruoyu Chen
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Weiyi Zhang
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Fan Song
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China
| | - Dan Cao
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
| | - Mingguang He
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong SAR, China.
| | - Danli Shi
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
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Vrettos K, Koltsakis E, Zibis AH, Karantanas AH, Klontzas ME. Generative adversarial networks for spine imaging: A critical review of current applications. Eur J Radiol 2024; 171:111313. [PMID: 38237518 DOI: 10.1016/j.ejrad.2024.111313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE In recent years, the field of medical imaging has witnessed remarkable advancements, with innovative technologies which revolutionized the visualization and analysis of the human spine. Among the groundbreaking developments in medical imaging, Generative Adversarial Networks (GANs) have emerged as a transformative tool, offering unprecedented possibilities in enhancing spinal imaging techniques and diagnostic outcomes. This review paper aims to provide a comprehensive overview of the use of GANs in spinal imaging, and to emphasize their potential to improve the diagnosis and treatment of spine-related disorders. A specific review focusing on Generative Adversarial Networks (GANs) in the context of medical spine imaging is needed to provide a comprehensive and specialized analysis of the unique challenges, applications, and advancements within this specific domain, which might not be fully addressed in broader reviews covering GANs in general medical imaging. Such a review can offer insights into the tailored solutions and innovations that GANs bring to the field of spinal medical imaging. METHODS An extensive literature search from 2017 until July 2023, was conducted using the most important search engines and identified studies that used GANs in spinal imaging. RESULTS The implementations include generating fat suppressed T2-weighted (fsT2W) images from T1 and T2-weighted sequences, to reduce scan time. The generated images had a significantly better image quality than true fsT2W images and could improve diagnostic accuracy for certain pathologies. GANs were also utilized in generating virtual thin-slice images of intervertebral spaces, creating digital twins of human vertebrae, and predicting fracture response. Lastly, they could be applied to convert CT to MRI images, with the potential to generate near-MR images from CT without MRI. CONCLUSIONS GANs have promising applications in personalized medicine, image augmentation, and improved diagnostic accuracy. However, limitations such as small databases and misalignment in CT-MRI pairs, must be considered.
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Affiliation(s)
- Konstantinos Vrettos
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Aristeidis H Zibis
- Department of Anatomy, Medical School, University of Thessaly, Larissa, Greece
| | - Apostolos H Karantanas
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece; Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
| | - Michail E Klontzas
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece; Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece.
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Saravi B, Guzel HE, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks. J Pers Med 2023; 13:1642. [PMID: 38138869 PMCID: PMC10744485 DOI: 10.3390/jpm13121642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Hamza Eren Guzel
- Department of Radiology, University of Health Sciences, Izmir Bozyaka Training and Research Hospital, Izmir 35170, Türkiye;
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
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Kim G, Baek J. Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image. Phys Med Biol 2023; 68:205007. [PMID: 37722388 DOI: 10.1088/1361-6560/acfadf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Objective.This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains.Approach.The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation.Main results.For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score.Significance.Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications.
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Affiliation(s)
- Gihun Kim
- School of Integrated Technology, Yonsei University, Republic of Korea
| | - Jongduk Baek
- Department of Artificial Intelligence, Yonsei University, Republic of Korea
- Baruenex Imaging, Republic of Korea
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Chan K, Maralani PJ, Moody AR, Khademi A. Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks. Front Neuroinform 2023; 17:1197330. [PMID: 37603783 PMCID: PMC10436214 DOI: 10.3389/fninf.2023.1197330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/18/2023] [Indexed: 08/23/2023] Open
Abstract
Introduction Acquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar maps from FLAIR MRI for the first time. Methods We evaluate a pix2pix model, two modified CycleGANs using paired and unpaired data, and a convolutional autoencoder in synthesizing DWI fractional anisotropy (FA) and mean diffusivity (MD) from whole FLAIR volumes. In total, 420 FLAIR and DWI volumes (11,957 images) from multi-center dementia and vascular disease cohorts were used for training/testing. Generated images were evaluated using two groups of metrics: (1) human perception metrics including peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), (2) structural metrics including a newly proposed histogram similarity (Hist-KL) metric and mean squared error (MSE). Results Pix2pix demonstrated the best performance both quantitatively and qualitatively with mean PSNR, SSIM, and MSE metrics of 23.41 dB, 0.8, 0.004, respectively for MD generation, and 24.05 dB, 0.78, 0.004, respectively for FA generation. The new histogram similarity metric demonstrated sensitivity to differences in fine details between generated and real images with mean pix2pix MD and FA Hist-KL metrics of 11.73 and 3.74, respectively. Detailed analysis of clinically relevant regions of white matter (WM) and gray matter (GM) in the pix2pix images also showed strong significant (p < 0.001) correlations between real and synthetic FA values in both tissue types (R = 0.714 for GM, R = 0.877 for WM). Discussion/conclusion Our results show that pix2pix's FA and MD models had significantly better structural similarity of tissue structures and fine details than other models, including WM tracts and CSF spaces, between real and generated images. Regional analysis of synthetic volumes showed that synthetic DWI images can not only be used to supplement clinical datasets, but demonstrates potential utility in bypassing or correcting registration in data pre-processing.
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Affiliation(s)
- Karissa Chan
- Electrical, Computer and Biomedical Engineering Department, Toronto Metropolitan University, Toronto, ON, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), Toronto, ON, Canada
| | - Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Alan R. Moody
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer and Biomedical Engineering Department, Toronto Metropolitan University, Toronto, ON, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), Toronto, ON, Canada
- Keenan Research Center, St. Michael’s Hospital, Toronto, ON, Canada
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GANs for Medical Image Synthesis: An Empirical Study. J Imaging 2023; 9:jimaging9030069. [PMID: 36976120 PMCID: PMC10055771 DOI: 10.3390/jimaging9030069] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets.
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Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. JOURNAL OF BIG DATA 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-022-00677-1.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
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Waisberg E, Ong J, Kamran SA, Zaman N, Paladugu P, Sarker P, Tavakkoli A, Lee AG. Further characterizing the physiological process of posterior globe flattening in spaceflight associated neuro-ocular syndrome with generative adversarial networks. J Appl Physiol (1985) 2023; 134:150-151. [PMID: 36592406 DOI: 10.1152/japplphysiol.00747.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Ethan Waisberg
- University College Dublin School of Medicine, Dublin, Ireland
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Nevada
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Nevada
| | - Phani Paladugu
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Nevada
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Nevada
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, Texas.,Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas.,The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas.,Department of Ophthalmology, Weill Cornell Medicine, New York City, New York.,Department of Neurology, Weill Cornell Medicine, New York City, New York.,Department of Neurosurgery, Weill Cornell Medicine, New York City, New York.,Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas.,University of Texas MD Anderson Cancer Center, Houston, Texas.,Texas A&M College of Medicine, Bryan, Texas.,Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa
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Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-378. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Meddeb A, Kossen T, Bressem KK, Hamm B, Nagel SN. Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen. Tomography 2021; 7:950-960. [PMID: 34941650 PMCID: PMC8704906 DOI: 10.3390/tomography7040078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen.
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Affiliation(s)
- Aymen Meddeb
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Hindenburgdamm 30, 12203 Berlin, Germany; (K.K.B.); (B.H.); (S.N.N.)
- Correspondence: ; Tel.: +49-30-450-527792
| | - Tabea Kossen
- CLAIM—Charité Lab for AI in Medicine, Charité—Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
| | - Keno K. Bressem
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Hindenburgdamm 30, 12203 Berlin, Germany; (K.K.B.); (B.H.); (S.N.N.)
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Bernd Hamm
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Hindenburgdamm 30, 12203 Berlin, Germany; (K.K.B.); (B.H.); (S.N.N.)
| | - Sebastian N. Nagel
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Hindenburgdamm 30, 12203 Berlin, Germany; (K.K.B.); (B.H.); (S.N.N.)
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Jia N, Zheng C. Emotion Speech Synthesis Method Based on Multi-Channel Time–Frequency Domain Generative Adversarial Networks (MC-TFD GANs) and Mixup. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06090-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractAs one of the most challenging and promising topics in speech field, emotion speech synthesis is a hot topic in current research. At present, the emotion expression ability, synthesis speed and robustness of synthetic speech need to be improved. Cycle-consistent Adversarial Networks (CycleGAN) provides a two-way breakthrough in the transformation of emotional corpus information. But there is still a gap between the real target and the synthesis speech. In order to narrow this gap, we propose an emotion speech synthesis method combining multi-channel Time–frequency Domain Generative Adversarial Networks (MC-TFD GANs) and Mixup. It includes three stages: multichannel Time–frequency Domain GANs (MC-TFD GANs), loss estimation based on Mixup and effective emotion region stacking based on Mixup. Among them, the gating unit GTLU (gated tanh linear units) and the image expression method of speech saliency region are designed. It combines the Time–frequency Domain MaskCycleGAN based on improved GTLU and the time-domain CycleGAN based on saliency region to form the multi-channel GAN in the first stage. Based on Mixup method, the calculation method of loss and the aggravation degree of emotion region are designed. Compared with several popular speech synthesis methods, the comparative experiments were carried out on the interactive emotional dynamic motion capture (IEMOCAP) corpus. The bi-directional three-layer long short-term memory (LSTM) model was used as the verification model. The experimental results showed that the mean opinion score (MOS) and the unweighted accuracy (UA) of the speech generated by the synthesis method were improved, and the improvements were 4% and 2.7%, respectively. The current model was superior to the existing GANs model in subjective evaluation and objective experiments, ensure that the speech generated by this model had higher reliability, better fluency and emotional expression ability.
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