BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation (2505.06133v1)

Abstract: The segmentation of substantial brain lesions is a significant and challenging task in the field of medical image segmentation. Substantial brain lesions in brain imaging exhibit high heterogeneity, with indistinct boundaries between lesion regions and normal brain tissue. Small lesions in single slices are difficult to identify, making the accurate and reproducible segmentation of abnormal regions, as well as their feature description, highly complex. Existing methods have the following limitations: 1) They rely solely on single-modal information for learning, neglecting the multi-modal information commonly used in diagnosis. This hampers the ability to comprehensively acquire brain lesion information from multiple perspectives and prevents the effective integration and utilization of multi-modal data inputs, thereby limiting a holistic understanding of lesions. 2) They are constrained by the amount of data available, leading to low sensitivity to small lesions and difficulty in detecting subtle pathological changes. 3) Current SAM-based models rely on external prompts, which cannot achieve automatic segmentation and, to some extent, affect diagnostic efficiency.To address these issues, we have developed a large-scale fully automated segmentation model specifically designed for brain lesion segmentation, named BrainSegDMLF. This model has the following features: 1) Dynamic Modal Interactive Fusion (DMIF) module that processes and integrates multi-modal data during the encoding process, providing the SAM encoder with more comprehensive modal information. 2) Layer-by-Layer Upsampling Decoder, enabling the model to extract rich low-level and high-level features even with limited data, thereby detecting the presence of small lesions. 3) Automatic segmentation masks, allowing the model to generate lesion masks automatically without requiring manual prompts.

Overview of BrainSegDMiF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation

The paper introduces a dynamic fusion-based approach to address challenges in brain lesion segmentation using the Segment Anything Model (SAM). The significant problem at hand involves the segmentation of brain lesions, characterized by high heterogeneity and indistinct boundaries, which remain difficult for traditional techniques. The authors propose BrainSegDMIF as a robust model that integrates multi-modal data for improved segmentation accuracy.

Methodological Innovations

The BrainSegDMIF model consists of three primary components:

1. Dynamic Modal Interactive Fusion (DMiF) Module: This module facilitates enhanced interaction and fusion of multi-modal imaging data. By integrating complementary information from modalities such as T1, T2, FLAIR, and T1CE MRI scans, the DMiF module optimizes feature extraction to improve the representation quality for subsequent analysis.
2. Layer-by-Layer Upsampling Decoder: To tackle the challenge posed by small lesions with blurred boundaries, the researchers designed a decoder that progressively upsamples features for increased sensitivity and accurate detection of these subtler pathological changes.
3. Prompt Generator for Automatic Segmentation: This addition allows the model to autonomously generate lesion masks without manual intervention or external prompts, thus improving efficiency and practical applicability.

Experimental Results

The model's performance was validated on two datasets: BraTS21 and FCD 2023. The BrainSegDMIF model yielded better performance than existing methods across several metrics, including Dice coefficient, IoU, Precision, and Recall. For instance, it achieved a Dice score of 79.64% on the BraTS21 dataset, representing a significant improvement over other state-of-the-art methods. This enhancement verifies the effectiveness of leveraging multi-modality inputs and decoder architecture to achieve superior segmentation results.

Implications and Future Directions

Practically, the model can significantly enhance clinical workflows by automating the segmentation process, reducing variability in manual delineation, and increasing diagnostic speed and accuracy—critical in planning interventions for brain parenchymal diseases. Theoretically, the paper exemplifies the potential of dynamic fusion strategies in medical image processing, setting a precedent for further examinations into multi-modal integration techniques.

Looking ahead, the continued optimization of SAM-based approaches holds promise for broader applications within medical imaging, potentially extending beyond brain lesion diagnostics. The development of systems accommodating multi-modal inputs could revolutionize AI-driven medical diagnostics, enabling comprehensive data synthesis and refined decision-making processes.

In conclusion, BrainSegDMIF presents a substantial advancement in brain lesion segmentation by effectively utilizing SAM architecture and dynamic fusion strategies. Its implementation into clinical scenarios can streamline evaluation processes and improve patient outcomes, fostering significant advancements in AI applications within healthcare environments.