Abstract
Accurate segmentation of breast tumors in magnetic resonance images (MRI) is essential for breast cancer diagnosis, yet existing methods face challenges in capturing irregular tumor shapes and effectively integrating local and global features. To address these limitations, we propose an uncertainty-gated deformable network to leverage the complementary information from CNN and Transformers. Specifically, we incorporates deformable feature modeling into both convolution and attention modules, enabling adaptive receptive fields for irregular tumor contours. We also design an Uncertainty-Gated Enhancing Module (U-GEM) to selectively exchange complementary features between CNN and Transformer based on pixel-wise uncertainty, enhancing both local and global representations. Additionally, a Boundary-sensitive Deep Supervision Loss is introduced to further improve tumor boundary delineation. Comprehensive experiments on two clinical breast MRI datasets demonstrate that our method achieves superior segmentation performance compared with state-of-the-art methods, highlighting its clinical potential for accurate breast tumor delineation.
Abstract (translated)
在磁共振成像（MRI）中精确分割乳腺肿瘤对于乳腺癌的诊断至关重要，然而现有的方法在捕捉不规则肿瘤形状和有效整合局部与全局特征方面面临挑战。为了解决这些限制，我们提出了一种不确定性门控可变形网络，该网络利用了卷积神经网络（CNN）和变换器模型所携带的信息优势。具体来说，我们将可变形特征建模融入到卷积模块和注意力模块中，使网络能够根据不规则肿瘤轮廓自适应地调整感受野范围。此外，我们设计了一种不确定性门控增强模块（U-GEM），该模块能够在像素级别上基于不确定度来选择性交换CNN与变换器模型之间的互补特征，从而提升局部和全局表示能力。同时引入了边界敏感深度监督损失函数以进一步改善肿瘤边界的划分精度。 通过在两个临床乳腺MRI数据集上的全面实验验证，我们的方法相较于当前最先进的技术表现出更优的分割性能，突显出其在精确乳腺肿瘤描绘方面的潜在临床应用价值。
URL
https://arxiv.org/abs/2509.15758
