  Accurate segmentation of ovarian tumors from medical images is crucial for
early diagnosis, treatment planning, and patient management. However, the
diverse morphological characteristics and heterogeneous appearances of ovarian
tumors pose significant challenges to automated segmentation methods. In this
paper, we propose MBA-Net, a novel architecture that integrates the powerful
segmentation capabilities of the Segment Anything Model (SAM) with
domain-specific knowledge for accurate and robust ovarian tumor segmentation.
MBA-Net employs a hybrid encoder architecture, where the encoder consists of a
prior branch, which inherits the SAM encoder to capture robust segmentation
priors, and a domain branch, specifically designed to extract domain-specific
features. The bidirectional flow of information between the two branches is
facilitated by the robust feature injection network (RFIN) and the domain
knowledge integration network (DKIN), enabling MBA-Net to leverage the
complementary strengths of both branches. We extensively evaluate MBA-Net on
the public multi-modality ovarian tumor ultrasound dataset and the in-house
multi-site ovarian tumor MRI dataset. Our proposed method consistently
outperforms state-of-the-art segmentation approaches. Moreover, MBA-Net
demonstrates superior generalization capability across different imaging
modalities and clinical sites.
