AI-enabled precise brain tumor segmentation by integrating Refinenet and contour-constrained features in MRI images.

BACKGROUND

Medical image segmentation is a fundamental task in medical image analysis and has been widely applied in multiple medical fields. The latest transformer-based deep learning segmentation model, Segment Anything Model (SAM), has demonstrated outstanding performance in natural image segmentation tasks through large-scale pre-training, achieving zero-shot image semantic understanding and pixel-level segmentation. However, medical images present challenges such as style variability, ill-defined object boundaries, and feature ambiguities, limiting the direct applicability of the SAM to medical image segmentation tasks.

PURPOSE

To enhance the robustness of the SAM in the domain of medical segmentation, we propose the SAM-RCCF framework. This approach aims to enhance the generalizability and precision of segmentation performance across diverse intracranial tumor types, including gliomas, metastatic tumors, and meningiomas.

MATERIALS AND METHODS

The study collected 484 axial T1-weighted contrast-enhanced (T1CE) magnetic resonance imaging (MRI) data of brain tumor patients, including 164 cases of glioma, 158 cases of metastatic tumors, and 162 cases of meningioma. All imaging data were randomly divided into training and testing sets. We employed the proposed SAM-RCCF model to perform segmentation experiments on these data, and five-fold cross-validation was adopted to evaluate the model's performance. This framework integrates the RefineNet module and the conditional control field with a conditional controller and Mask generator, enabling precise feature recognition and tailored segmentation for medical images, optimizing segmentation accuracy RESULTS: In the glioma segmentation experiment, the SAM-RCCF model achieved outstanding performance with an IOU of 0.90, DSC of 0.912, and HD of 13.13. For the meningioma segmentation task, it obtained an IOU of 0.9214, DSC of 0.93, and HD of 11.41, significantly outperforming other classic segmentation models.

CONCLUSION

The segmentation experiment results demonstrate that in the segmentation tasks of glioma, metastatic tumors, and meningioma MRI images, the SAM-RCCF algorithm significantly outperformed the original SAM in terms of DSC, HD, and IOU segmentation performance metrics. The experimental results verify the effectiveness of the SAM-RCCF framework in segmenting complex and variable brain tumor images, enhancing segmentation accuracy and robustness.