# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 1 Introduction  
Heart disease remains one of the leading causes of mortality worldwide, with atrial fibrillation (AF) being a significant contributor to the global burden of cardiac disorders. AF is strongly associated with the presence of left atrial (LA) fibrosis, which serves as an essential biomarker for disease progression and risk stratification [1, 2]. Accurate segmentation of the LA from medical imaging, particularly 3D Late Gadolinium-Enhanced (LGE) MRI, is indispensable for the identification of LA fibrosis. This segmentation is critical for informing clinical diagnosis, planning therapeutic interventions such as catheter ablation, and assessing long-term patient outcomes [2, 3, 4, 5]. However, the current practice of manual LA segmentation is labor-intensive, subjective, and prone to inter-observer variability, making it impractical for large-scale clinical adoption [6, 7, 8]. Semantic segmentation, a pixel-wise classification task, provides a robust solution to these challenges by accurately delineating the LA and distinguishing it from adjacent cardiac structures. Despite its promise, LA segmentation faces significant hurdles due to the complex anatomical shapes of the LA, patient-specific variations, low image contrast, and background noise [6; 9]. Traditional atlas-based segmentation methods, while effective for other anatomical regions, have consistently underperformed for LA segmentation. These limitations have accelerated the adoption of deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), which have demonstrated remarkable success in cardiac imaging tasks [10; 11; 12; 13]. Among deep learning approaches, CNN-based architectures such as U-Net and V-Net have become foundational for medical image segmentation. These models leverage encoder-decoder structures with skip connections to recover spatial information and achieve detailed segmentation [14]. During the 2018 Atrial Segmentation Challenge, CNN-based methods outperformed traditional atlas-based approaches by approximately 7% in Dice score [15]. Modifications of U-Net, such as nnU-Net [16] and GCW-UNet [17], have further enhanced segmentation performance by incorporating additional modules, including Gaussian blurring, channel weighting, and spatial pyramid pooling. GCW-UNet, for example, achieved a Dice score of 93.57% on the challenge dataset by using Gaussian-blurred images and a channel weight module to capture intricate LA details. While 2D CNNs have shown promise, they often fail to account for inter-slice correlations in 3D imaging, leading to inaccuracies in boundary delineation [18; 19; 20]. On the other hand, single-stage 3D CNNs, which process entire 3D volumes, provide better contextual understanding but suffer from high computational costs and memory demands [21]. For instance, the fourth-ranking model in the 2018 challenge, a 3D CNN by [21], utilized dilated convolutions and residual connections to enhance feature extraction but required 104 million parameters, making it 50 times larger than the smallest competing model. To mitigate these inefficiencies, two-stage segmentation frameworks have been proposed, where the first stage identifies a region of interest (ROI) and the second stage performs fine segmentation [22; 23; 24]. While effective, these methods are prone to error propagation between stages, potentially compromising overall accuracy. Moreover, [PERSON] and [PERSON] proposed the NFMPAtt-Unet model for medical image segmentation to address blurry edges and generalizability [25]. They introduced a Multiscale Dynamic Weight Feature Pyramid, Hybrid Weighted Attention, and Neighborhood Rough Set-based Fuzzy c-means to improve the feature extraction from the model. [PERSON] et al. proposed the DDMARN model for pixel-level classification of membranous nephropathy using hyperspectral imaging [26]. They introduced a Dimension-Driven Multi-Path Attention Residual Block to extract multi-scale spatial-spectral features, utilizing a channel attention mechanism for enhanced feature expression.