Deep learning-based segmentation methods are widely utilized for detecting lesions in ultrasound images. Throughout the imaging procedure, the attenuation and scattering of ultrasound waves cause contour blurring and the formation of artifacts, limiting the clarity of the acquired ultrasound images. To overcome this challenge, we propose a contour-based probabilistic segmentation model CP-UNet, which guides the segmentation network to enhance its focus on contour during decoding. We design a novel down-sampling module to enable the contour probability distribution modeling and encoding stages to acquire global-local features. Furthermore, the Gaussian Mixture Model utilizes optimized features to model the contour distribution, capturing the uncertainty of lesion boundaries. Extensive experiments with several state-of-the-art deep learning segmentation methods on three ultrasound image datasets show that our method performs better on breast and thyroid lesions segmentation.

翻译：基于深度学习的分割方法被广泛用于超声图像中的病灶检测。在整个成像过程中，超声波的衰减和散射会导致轮廓模糊及伪影形成，从而限制所获取超声图像的清晰度。为克服这一挑战，我们提出了一种基于轮廓的概率分割模型CP-UNet，该模型在解码阶段引导分割网络增强对轮廓的关注。我们设计了一种新颖的下采样模块，使轮廓概率分布建模与编码阶段能够获取全局-局部特征。此外，高斯混合模型利用优化后的特征对轮廓分布进行建模，从而捕捉病灶边界的不确定性。在三个超声图像数据集上，与多种先进的深度学习分割方法进行的广泛实验表明，我们的方法在乳腺和甲状腺病灶分割任务上表现更优。