针对超声图像轮廓模糊问题，提出基于概率的轮廓分割模型提升边界识别精度。
CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation
- 引入轮廓概率建模，引导网络聚焦病变边界进行分割
- 使用高斯混合模型捕捉边界不确定性，提升对模糊轮廓的鲁棒性
- 在乳腺和甲状腺病变数据集上优于现有方法，适合医学图像精准分割场景
基于深度学习的分割方法广泛应用于超声图像中病灶检测。由于超声波衰减与散射，成像过程中常出现轮廓模糊和伪影，影响图像清晰度。为解决该问题，本文提出一种基于轮廓的概率分割模型CP-UNet，通过在解码阶段引导网络关注轮廓信息来增强分割效果。设计新型下采样模块，使轮廓概率分布建模与编码阶段能够同时获取全局与局部特征。此外，利用优化后的特征，采用高斯混合模型（GMM）对轮廓分布进行建模，有效捕捉病变边界的不确定性。在三个超声图像数据集上，与多个先进深度学习分割方法对比，本方法在乳腺和甲状腺病变分割任务中表现更优。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。