Core Concepts
A novel unsupervised tumor-aware distillation teacher-student network (UTAD-Net) that can accurately perceive and translate tumor areas to generate realistic multi-modal brain images without paired data.
Abstract
The content discusses the problem of obtaining fully paired multi-modal brain images in practice due to various factors, leading to modality-missing brain images. To address this, the authors propose an unsupervised tumor-aware distillation teacher-student network called UTAD-Net.
The key highlights are:
- UTAD-Net consists of a teacher network and a student network. The teacher network learns an end-to-end mapping from source to target modality using unpaired images and corresponding tumor masks.
- The translation knowledge is then distilled into the student network, enabling it to generate more realistic tumor areas and whole images without masks.
- Experiments show that UTAD-Net achieves competitive performance on both quantitative and qualitative evaluations compared to state-of-the-art methods.
- The generated images by UTAD-Net are also demonstrated to be effective for improving downstream brain tumor segmentation tasks.
Stats
Multi-modal brain images from MRI scans are widely used in clinical diagnosis to provide complementary information.
Obtaining fully paired multi-modal images is challenging due to various factors, resulting in modality-missing brain images.
Quotes
"Multi-modal brain images from MRI (Magnetic Resonance Imaging) scans are widely used in various clinical scenarios[1], [2]. These images are further divided into several modalities(sequences), such as T1-weighted (T1), T1-with-contrast-enhanced (T1ce), T2-weighted (T2), T2-fluid-attenuated inversion recovery (Flair), etc."
"Existing methods for multi-modal image translation have shown promising results in natural images. However, when applied to medical images, particularly brain tumor images, the results are often unsatisfactory[3]."