Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.
翻译：医学图像分割对于诊断、治疗规划和疾病监测至关重要，但常受模糊边缘和背景噪声等复杂因素挑战。我们提出EEMS这一新型分割模型，它结合了边缘感知增强单元（EAEU）与多尺度提示生成单元（MSPGU）。EAEU通过多频特征提取增强边缘感知能力，从而精确定义边界。MSPGU采用提示引导方法融合高层语义特征与低层空间特征，确保目标定位的精确性。双源自适应门控融合单元（DAGFU）将EAEU的边缘特征与MSPGU的语义特征进行融合，提升了分割精度与鲁棒性。在ISIC2018等数据集上的测试验证了EEMS作为临床工具的优越性能与可靠性。
相关内容
微信扫码咨询专知VIP会员