ArticleFrontiers in radiology2023
Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors.
Article in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- Application of generative artificial intelligence (AI) to support pain neuroscience research in persons who are pregnant.Communications medicine · 2026Article
- MedGAN-SSM: Multimodal brain image synthesis network integration using SSM empowered GAN.iScience · 2026Article
- BT-CAP: a subcomponent-aware and anatomically constrained data augmentation framework for multi-modal brain tumor MRI segmentation.Frontiers in radiology · 2026Article
- Accurate and robust segmentation of cerebral distal small arteries by DVNet with dual contextual path and vascular attention enhancement.Quantitative imaging in medicine and surgery · 2025Article
- Similarity and quality metrics for MR image-to-image translation.Scientific reports · 2025Article
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4 authors.
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Abstract
Challenging tasks such as lesion segmentation, classification, and analysis for the assessment of disease progression can be automatically achieved using deep learning (DL)-based algorithms. DL techniques such as 3D convolutional neural networks are trained using heterogeneous volumetric imaging data such as MRI, CT, and PET, among others. However, DL-based methods are usually only applicable in the presence of the desired number of inputs. In the absence of one of the required inputs, the method cannot be used. By implementing a generative adversarial network (GAN), we aim to apply multi-label automatic segmentation of brain tumors to synthetic images when not all inputs are present. The implemented GAN is based on the Pix2Pix architecture and has been extended to a 3D framework named Pix2PixNIfTI. For this study, 1,251 patients of the BraTS2021 dataset comprising sequences such as T
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