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Computerized medical imaging and graphics, 2024-03, Vol.112, p.102332-102332, Article 102332
2024
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Autor(en) / Beteiligte
Titel
Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation
Ist Teil von
  • Computerized medical imaging and graphics, 2024-03, Vol.112, p.102332-102332, Article 102332
Ort / Verlag
United States: Elsevier Ltd
Erscheinungsjahr
2024
Quelle
MEDLINE
Beschreibungen/Notizen
  • Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However, obtaining all required sequences and expertly labeled data for training is challenging and can result in decreased quality of segmentation models developed through automated algorithms. In this work, we examine the possibility of employing a conditional generative adversarial network (GAN) approach for synthesizing multi-modal images to train deep learning-based neural networks aimed at high-grade glioma (HGG) segmentation. The proposed GAN is conditioned on auxiliary brain tissue and tumor segmentation masks, allowing us to attain better accuracy and control of tissue appearance during synthesis. To reduce the domain shift between synthetic and real MR images, we additionally adapt the low-frequency Fourier space components of synthetic data, reflecting the style of the image, to those of real data. We demonstrate the impact of Fourier domain adaptation (FDA) on the training of 3D segmentation networks and attain significant improvements in both the segmentation performance and prediction confidence. Similar outcomes are seen when such data is used as a training augmentation alongside the available real images. In fact, experiments on the BraTS2020 dataset reveal that models trained solely with synthetic data exhibit an improvement of up to 4% in Dice score when using FDA, while training with both real and FDA-processed synthetic data through augmentation results in an improvement of up to 5% in Dice compared to using real data alone. This study highlights the importance of considering image frequency in generative approaches for medical image synthesis and offers a promising approach to address data scarcity in medical imaging segmentation. •Utilizing a conditional GAN to synthesize multi-modal brain tumor images for deep learning-based segmentation networks.•Fourier domain adaptation (FDA) used to align synthetic and real images, reducing domain shift.•FDA demonstrates up to 5% improvement in segmentation performance and prediction confidence.•Study emphasizes the importance of considering image frequency in generative medical image synthesis approaches.
Sprache
Englisch
Identifikatoren
ISSN: 0895-6111
eISSN: 1879-0771
DOI: 10.1016/j.compmedimag.2024.102332
Titel-ID: cdi_proquest_miscellaneous_2917552681

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