MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation

TL;DR

MBA-Net is a novel segmentation architecture that combines SAM-based priors with domain-specific features, achieving accurate ovarian tumor segmentation across ultrasound and MRI images with strong generalization.

Contribution

The paper introduces MBA-Net, integrating SAM with domain knowledge through a hybrid encoder and bidirectional information flow for improved tumor segmentation.

Findings

Outperforms state-of-the-art methods on multiple datasets

Demonstrates strong generalization across imaging modalities

Achieves robust segmentation with high accuracy

Abstract

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.