Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).
Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-processing for BMs detection and segmentation with BB images.
The BB images of 50 patients were collect to train (40) and test (10) the DL model. To ensure consistency, we implemented piecewise linear histogram matching for intensity normalization and resampling. Modified U-Net, including combination with generative adversarial network (GAN), was applied to enhance the segmentation performance. The U-Net-based networks generated bounding boxes indicating regions of interest, which were then processed in a post-processing using the Segment Anything Model (SAM). We quantitatively assessed the three U-Net-based models and their post-processed counterparts in terms of lesion-wise sensitivity (LWS), patient-wise dice similarity coefficient (DSC), and average false-positive rate (FPR).
The modified U-Net with GAN yielded a patient-wise DSC of 0.853 and a LWS of 89.19%, which outperformed the standard U-Net (patient-wise DSC of 0.815) and modified U-Net only (patient-wise DSC of 0.846). Combining GAN architecture with modified U-Net also reduced the FPR, less than 1 on average. Post-processing with SAM further did not affect LWS and FPR, but effectively enhanced the patient-wise DSC by 2%-3% for the U-Net-based models.
The modifications to standard U-Net notably improves the detection and segmentation of BMs in BB images, and applying SAM as post-processing can further enhance the precision of segmentation results.
The BB images of 50 patients were collect to train (40) and test (10) the DL model. To ensure consistency, we implemented piecewise linear histogram matching for intensity normalization and resampling. Modified U-Net, including combination with generative adversarial network (GAN), was applied to enhance the segmentation performance. The U-Net-based networks generated bounding boxes indicating regions of interest, which were then processed in a post-processing using the Segment Anything Model (SAM). We quantitatively assessed the three U-Net-based models and their post-processed counterparts in terms of lesion-wise sensitivity (LWS), patient-wise dice similarity coefficient (DSC), and average false-positive rate (FPR).
The modified U-Net with GAN yielded a patient-wise DSC of 0.853 and a LWS of 89.19%, which outperformed the standard U-Net (patient-wise DSC of 0.815) and modified U-Net only (patient-wise DSC of 0.846). Combining GAN architecture with modified U-Net also reduced the FPR, less than 1 on average. Post-processing with SAM further did not affect LWS and FPR, but effectively enhanced the patient-wise DSC by 2%-3% for the U-Net-based models.
The modifications to standard U-Net notably improves the detection and segmentation of BMs in BB images, and applying SAM as post-processing can further enhance the precision of segmentation results.