FReMCI: Frequency-Reliable Memory Cross Intervention for Semi-Supervised Ultrasound Image Segmentation

Semi-supervised ultrasound image segmentation is clinically attractive because dense annotations are costly, but probe-dependent appearance, speckle noise, low contrast, and ambiguous boundaries make unlabeled supervision unreliable. Existing methods can propagate errors through pseudo labels, spatially mixed regions, and weak foreground features. We present FReMCI, a frequency-reliable memory cross intervention framework that controls cross-sample transfer at frequency, region, and feature-memory levels. Phase-Preserved Low-Frequency Amplitude Sharing (PLAS) extends Fourier amplitude interpolation to paired labeled–unlabeled images while retaining each image's phase and high-frequency amplitude. Reliability-Guided Semantic Region Intervention (ReSRI) combines confidence, entropy, and class margin from an EMA teacher with teacher–student agreement to construct reliability-weighted pseudo targets and foreground-centered bidirectional region exchanges with seam-suppressed supervision. Memory Cross Injection (MCI) stores labeled and reliable unlabeled foreground features and injects opposite-source context into the student decoder; teacher predictions remain independent of the memory state. Inference uses the single student segmentation network. Under the reported fixed, single-run protocol on five ultrasound tasks from four dataset sources, FReMCI ranks highest in average Dice across all three labeled-data protocols (1/10, 3/10, and 1/2), outperforming the strongest competing averages by up to 3.87 points.