3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum

2026-05-29

AI Summary

This study addresses the challenge of early and precise diagnosis of placenta accreta spectrum (PAS) disorders in primary hospitals, where expert resources are scarce. To this end, the authors present the first MRI dataset for PAS with fine-grained annotations and propose a novel framework, 3DSAMba. This approach integrates medical prior knowledge into the 3D Segment Anything Model via an adapter mechanism and incorporates a multi-level aggregation Mamba (MLAM) alongside a fused state space model (FSSM) to effectively capture and combine multi-scale features. The proposed method significantly enhances 3D lesion segmentation accuracy, thereby substantially improving automated PAS diagnosis performance. Both the curated dataset and the implementation code have been made publicly available to foster further research in this domain.

Abstract

Placenta Accreta Spectrum (PAS) is a rare but highly dangerous obstetric disease. Early and accurate PAS diagnosis is critical for maternal health. Traditional PAS diagnosis relies on experienced doctors by analyzing the cesarean history and Magnetic Resonance Imaging (MRI) data. However, district-level hospitals often lack the expertise and resources for accurate PAS diagnosis. To address these challenges, we establish the first MRI-based PAS dataset, which includes both fine-grained segmentation and classification annotations. Meanwhile, diagnosing PAS can be significantly enhanced by segmenting lesion areas from MRI images of the uterus. To achieve automatic PAS diagnosis, we propose 3DSAMba, a novel feature learning framework for effective lesion segmentation. More specifically, we first design a 3D Segment Anything Model (SAM) and incorporate medical domain information into the model through an efficient adapter mechanism. In addition, we introduce a Multi-Level Aggregation Mamba (MLAM) to aggregate feature maps across different levels and a Fusion State Space Model (FSSM) to fuse multi-scale features from both the encoder and decoder. Finally, we apply segmentation masks to the original MRI images through element-wise multiplication, effectively isolating lesion areas for more accurate PAS diagnosis. Extensive experiments validate that our framework significantly improves the PAS diagnostic performance. To facilitate further research in PAS diagnosis, we have released the dataset and source code at https://github.com/Drchip61/PASD.

Problem

• Placenta Accreta Spectrum
• MRI segmentation
• lesion segmentation
• automatic diagnosis
• obstetric disease

Innovation

• 3D Segment Anything Model
• Visual Mamba
• Multi-Level Aggregation Mamba
• Fusion State Space Model
• Placenta Accreta Spectrum