A2DMN：用于乳腺超声语义分割的解剖感知膨胀多尺度网络
A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation
AI总结：
针对乳腺超声图像分割中因缺乏解剖先验导致误分类和下采样导致边界不准的问题，提出解剖感知膨胀多尺度网络A2DMN及平滑项，融合多尺度上下文生成准确边界，显著提升肌肉、乳腺和肿瘤的分割精度。
摘要：
近年来，用于乳腺超声（BUS）图像语义分割的卷积神经网络取得了巨大成功；然而，仍存在两个主要挑战。1）大多数当前方法本质上缺乏利用组织解剖结构的能力，导致图像区域被误分类。2）由于反复的下采样操作，它们难以产生准确的边界。为了解决这些问题，我们提出了一种新颖的乳腺解剖感知网络，用于捕捉精细的图像细节，并提出了一种编码乳腺解剖结构的新平滑项。它融合了跨多个空间尺度的上下文信息，以生成更准确的语义边界。我们进行了大量实验，使用包含325张图像的BUS数据集将所提出的方法与八种最先进的方法进行比较。结果表明，所提出的方法显著改善了肌肉、乳腺和肿瘤类别的分割，并产生了更准确的组织边界精细细节。
英文摘要：
In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) Most current approaches inherently lack the ability to utilize tissue anatomy, resulting in misclassified image regions. 2) They struggle to produce accurate boundaries due to the repeated down-sampling operations. To address these issues, we propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. Extensive experiments are conducted to compare the proposed method and eight state-of-the-art approaches using a BUS dataset with 325 images. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes and produces more accurate fine details of tissue boundaries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
