Accurate segmentation of lymphoma and metastatic tumor regions in ultrasound imaging is clinically crucial but remains challenging due to inherent modality artifacts, low lesion-to-parenchyma contrast, and particularly the heterogeneous echogenicity obscuring precise lesion boundary delineation. The scarcity of pixel-level expert annotations further impedes fully supervised deep learning solutions. To address this, we propose BGPCNet, a novel semi-supervised segmentation network that effectively leverages both limited labeled data and abundant unlabeled data by synergistically integrating Frequency Consistency Module(FCM) and Boundary Guided Patch Contrast(BGPC). Specifically, the FCM establishes robust structural coherence across different frequency sub-bands. It employs Discrete Wavelet Transform (DWT) to decompose spatial features into four frequency components, enhances each using Visual State Space (VSS) blocks inspired by VMamba, and enforces consistency between student and teacher model features in both spatial and frequency domains via a dedicated frequency consistency loss. Concurrently, the BGPC module explicitly tackles ambiguous boundary regions by formulating a contrastive learning strategy. It treats patches containing structure boundaries and central structure patches as positive and negative samples for each other, driving the model to learn discriminative features that better distinguish boundary pixels from the background and internal regions. Built upon the Mean Teacher framework with supervised pre-training, BGPCNet optimizes a combined loss function incorporating supervised loss, pixel-wise consistency loss, frequency consistency loss, and BGPC loss. Comprehensive evaluations on a large-scale private clinical superficial lymph node dataset and the public TN3k thyroid nodule dataset demonstrate BGPCNet’s superiority. It significantly outperforms state-of-the-art semi-supervised methods across key metrics.