| 1. |
- Akbar, Muhammad Usman, 1990-, et al.
(författare)
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Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models
- 2024
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Ingår i: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%–90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.
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| 2. |
- Ericsson, Leon, et al.
(författare)
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Generalized super-resolution 4D Flow MRI - using ensemble learning to extend across the cardiovascular system
- 2024
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Ingår i: IEEE journal of biomedical and health informatics. - 2168-2194 .- 2168-2208. ; , s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was therefore to explore the generalizability of SR 4D Flow MRI using a combination of existing super-resolution base models, novel heterogeneous training sets, and dedicated ensemble learning techniques; the latter-most being effectively used for improved domain adaption in other domains or modalities, however, with no previous exploration in the setting of 4D Flow MRI. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with the novel use of ensemble learning in the setting of advanced fullfield flow imaging extending utility across various clinical areas of interest.
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| 3. |
- Gustafsson, Christian Jamtheim, et al.
(författare)
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Federated training of segmentation models for radiation therapy treatment planning
- 2024
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Ingår i: Radiotherapy and Oncology. - 0167-8140 .- 1879-0887. ; 194, s. S4819-S4822
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Tidskriftsartikel (refereegranskat)abstract
- Radiotherapy treatment planning takes substantial time, several hours per patient, as it involves manual segmentation of tumor and risk organs. Segmentation networks can be trained to automatically perform the segmentations, but typically require large annotated datasets for training. Sharing of sensitive data between hospitals, to create a larger dataset, is often difficult due to ethics and GDPR. Here we therefore demonstrate that federated learning is a solution to this problem, as then only the segmentation model is sent between each hospital and a global server. We export and preprocess brain tumor images from the oncology departments in Linköping and Lund, and use federated learning to train a global segmentation model using two different frameworks.
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