HilbertMamba: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos

TL;DR

This paper introduces LGRNet, a novel network for uterine fibroid segmentation in ultrasound videos, utilizing local-global reciprocal temporal context propagation, and presents a new annotated dataset for this task.

Contribution

The paper proposes LGRNet with Cyclic Neighborhood Propagation and Hilbert Selective Scan for improved temporal context modeling, along with the first annotated ultrasound video dataset for fibroid segmentation.

Findings

LGRNet outperforms state-of-the-art methods on UFUV and VPS datasets.

The proposed methods effectively distinguish fibroids from noisy tissues.

The dataset and code are publicly available for further research.

Abstract

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