  Regular screening and early discovery of uterine fibroid are crucial for
preventing potential malignant transformations and ensuring timely, life-saving
interventions. To this end, we collect and annotate the first ultrasound video
dataset with 100 videos for uterine fibroid segmentation (UFUV). We also
present Local-Global Reciprocal Network (LGRNet) to efficiently and effectively
propagate the long-term temporal context which is crucial to help distinguish
between uninformative noisy surrounding tissues and target lesion regions.
Specifically, the Cyclic Neighborhood Propagation (CNP) is introduced to
propagate the inter-frame local temporal context in a cyclic manner. Moreover,
to aggregate global temporal context, we first condense each frame into a set
of frame bottleneck queries and devise Hilbert Selective Scan (HilbertSS) to
both efficiently path connect each frame and preserve the locality bias. A
distribute layer is then utilized to disseminate back the global context for
reciprocal refinement. Extensive experiments on UFUV and three public Video
Polyp Segmentation (VPS) datasets demonstrate consistent improvements compared
to state-of-the-art segmentation methods, indicating the effectiveness and
versatility of LGRNet. Code, checkpoints, and dataset are available at
https://github.com/bio-mlhui/LGRNet
