Ultrahigh-field (UHF) magnetic resonance imaging (MRI), 7T MRI, provides superior anatomical details of internal brain structures thanks to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a SegUHF that systematically fuses the input LF MRI feature representations with the inferred 7T-like feature representations for brain image segmentation tasks in a 7T-absent environment. Specifically, our proposed adaptive fusion module within the SegUHF aggregates 7T-like features derived from the LF image using a pre-trained network and then refines them to effectively assimilate UHF guidance into LF image features. Using intensity-guided features obtained from such aggregation and assimilation, segmentation models can recognize subtle structural representations that are usually difficult to identify when relying only on LF features. Beyond these advantages, this strategy can be seamlessly utilized by modulating the contrast of LF features in alignment with UHF guidance, even when employing arbitrary segmentation models. Extensive experiments demonstrated that our method outperformed all baselines in both brain tissue and whole-brain segmentation, while also showcasing adaptability and scalability across various models and tasks. Code is available at https://github.com/ku-milab/UHF-guided_segmentation

Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using only one source domain data to segment data from an unseen target domain. Despite substantial advances in SDG with data augmentation, existing methods often fail to fully consider the details and uncertain areas prevalent in MIS, leading to mis-segmentation. In this study, we propose a Fourier-based semantic augmentation method called FIESTA using uncertainty guidance (UG) to enhance the fundamental goals of MIS in an SDG context by manipulating the amplitude and phase components in the frequency domain. The proposed Fourier augmentative transformer (FAT) addresses semantic amplitude modulation based on meaningful angular points to induce pertinent variations and harnesses the phase spectrum to ensure structural coherence. Moreover, FIESTA employs uncertainty estimation to fine-tune the augmentation process, improving the ability of the model to adapt to diverse augmented data and concentrate on areas with higher ambiguity. Extensive experiments across three cross-domain scenarios demonstrate that FIESTA surpasses recent state-of-the-art SDG approaches in segmentation performance and significantly contributes to boosting the model's applicability in medical imaging modalities.

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the distributional gap between training and testing samples—a problem known as domain shift. Researchers have dedicated their efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution (OOD) data distributions. This article comprehensively reviews domain generalization (DG) studies specifically tailored for MedIA. We provide a holistic view of how DG techniques interact within the broader MedIA system, going beyond methodologies to consider the operational implications on the entire MedIA workflow. Specifically, we categorize DG methods into data-level, feature-level, model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL equipped from data acquisition to model prediction and analysis. Furthermore, we critically analyze the strengths and weaknesses of various methods, unveiling future research opportunities.

Face age transformation aims to convert reference images into synthesized images so that they portray the specified target ages. The crux of this task is to change only age-related areas of the given image while maintaining the age-irrelevant areas unchanged. Nevertheless, a common limitation among most existing models is the struggle to generate high-quality aging images that effectively consider both crucial properties. To address this problem, we propose a novel GAN-based face-aging framework that utilizes age-aware Guidance via Masking-Based Attention (GMBA). Specifically, we devise an age-aware guidance module to adjust age-relevant and age-irrelevant attributes within the image seamlessly. By virtue of its capability, it enables the model to produce realistic age-transformed images that certainly preserve the input's identities while delicately imposing age-related properties. Experimental results show that our proposed GMBA outperformed other state-of-the-art methods in terms of identity preservation and accurate age conversion, as well as providing superior visual quality for age-transformed images.

Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies diagnostic model learning, visual explanation generation (explanation unit), and trained diagnostic model reinforcement (reinforcement unit) guided by the visual explanation. For the visual explanation, we generate a counterfactual map that transforms an input sample to be identified as an intended target label. For example, a counterfactual map can localize hypothetical abnormalities within a normal brain image that may cause it to be diagnosed with Alzheimer's disease (AD). We believe that the generated counterfactual maps represent data-driven knowledge about a target task, i.e., AD diagnosis using structural MRI, which can be a vital source of information to reinforce the generalization of the trained diagnostic model. To this end, we devise an attention-based feature refinement module with the guidance of the counterfactual maps. The explanation and reinforcement units are reciprocal and can be operated iteratively. Our proposed approach was validated via qualitative and quantitative analysis on the ADNI dataset. Its comprehensibility and fidelity were demonstrated through ablation studies and comparisons with existing methods.