Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Kossen, Tabea; Hirzel, Manuel A.; Madai, Vince I.; Boenisch, Franziska; Hennemuth, Anja; Hildebrand, Kristian; Pokutta, Sebastian; Sharma, Kartikey; Hilbert, Adam; Sobesky, Jan; Galinović, Ivana; Khalil, Ahmed A.; Fiebach, Jochen B.; Frey, Dietmar

doi:10.3389/frai.2022.813842

Cited by 6 publications

(2 citation statements)

References 34 publications

(47 reference statements)

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“…Ziller et al [5] proposed the training of a segmentation network for CXR images using a discriminative model trained with DP-SGD. Kossen et al [6] proposed the generation of differentially private time-of-flight magnetic resonance angiography (TOF-MRA) images using generative adversarial networks (GANs) trained with DP-SGD. For DP-SGD, the theoretical guarantee that images generated using GANs trained with DP-SGD satisfy -LDP is not apparent.…”

Section: Related Workmentioning

confidence: 99%

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Shibata,

Hanaoka,

Cao

et al. 2023

Applied Sciences

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Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI.

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Section: Related Workmentioning

confidence: 99%

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Shibata,

Hanaoka,

Cao

et al. 2023

Applied Sciences

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“…It has the potential to generate synthetic data that appear authentic, so resolving the privacy issue. Recently, Kossen et al [93] attempted a similar task. However, due to the sensitivity of brain data, research is currently ongoing to determine the quality of synthetic data.…”

Section: B Challenges With Deep Learning In Bvsmentioning

confidence: 99%

Brain Vessel Segmentation Using Deep Learning—A Review

et al. 2022

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This article provides a comprehensive review of deep learning-based blood vessel segmentation of brain. The cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in future to develop deep learning solution.

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A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels.

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Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Cited by 6 publications

References 34 publications

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Brain Vessel Segmentation Using Deep Learning—A Review

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

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