Abstract
Medical ultrasound videos are widely used for medical inspections, disease diagnosis and surgical planning. High-fidelity lesion area and target organ segmentation constitutes a key component of the computer-assisted surgery workflow. The low contrast levels and noisy backgrounds of ultrasound videos cause missegmentation of organ boundary, which may lead to small object losses and increase boundary segmentation errors. Object tracking in long videos also remains a significant research challenge. To overcome these challenges, we propose a memory bank-based wavelet filtering and fusion network, which adopts an encoder-decoder structure to effectively extract fine-grained detailed spatial features and integrate high-frequency (HF) information. Specifically, memory-based wavelet convolution is presented to simultaneously capture category, detailed information and utilize adjacent information in the encoder. Cascaded wavelet compression is used to fuse multiscale frequency-domain features and expand the receptive field within each convolutional layer. A long short-term memory bank using cross-attention and memory compression mechanisms is designed to track objects in long video. To fully utilize the boundary-sensitive HF details of feature maps, an HF-aware feature fusion module is designed via adaptive wavelet filters in the decoder. In extensive benchmark tests conducted on four ultrasound video datasets (two thyroid nodule, the thyroid gland, the heart datasets) compared with the state-of-the-art methods, our method demonstrates marked improvements in segmentation metrics. In particular, our method can more accurately segment small thyroid nodules, demonstrating its effectiveness for cases involving small ultrasound objects in long video. The code is available at this https URL.
Abstract (translated)
医学超声视频广泛用于医疗检查、疾病诊断和手术规划。高保真病变区域和目标器官的分割是计算机辅助手术工作流程中的关键组成部分。由于超声视频中的低对比度水平和噪声背景，会导致器官边界误分割，从而可能引发小对象丢失并增加边界分割误差。在长视频中进行对象跟踪仍然是一个重要的研究挑战。为了解决这些难题，我们提出了一种基于记忆库的波let滤波与融合网络，该网络采用了编码器-解码器结构以有效提取细粒度的空间特征，并整合高频（HF）信息。具体来说，在编码器部分提出了基于内存的波let卷积方法，同时捕捉类别、细节信息并利用相邻信息。通过级联的波let压缩来融合多尺度频域特性并扩大每个卷积层的感受野。设计了一个使用交叉注意力和记忆压缩机制的长短期记忆库，用于在长时间视频中跟踪对象。为了充分利用特征图中的边界敏感高频细节，在解码器中通过自适应波let滤波器设计了具有HF感知功能的特征融合模块。 在四个超声视频数据集（两个甲状腺结节、甲状腺腺体和心脏数据集）上进行广泛的基准测试，与现有方法相比，我们的方法在分割指标方面表现出显著改进。特别是，我们方法能够更准确地分割小甲状腺结节，证明了其对长时间视频中小超声对象的有效性。 代码可在[此处](https://this-is-the-code-location.com)获取（请将URL替换为实际链接）。
URL
https://arxiv.org/abs/2512.15066
