Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block.

BACKGROUND

Deep learning has significantly improved medical image segmentation, particularly in complex applications such as gynecological brachytherapy (BT). This study proposes a novel U-Net variant, the residual in residual dense block U-Net (RU-Net), to address the segmentation of organs-at-risk (OARs), which are often distorted due to BT probe insertion. Accurate segmentation is essential for effective treatment planning; however, task-specific datasets are scarce. To tackle this, a dedicated magnetic resonance imaging (MRI) dataset was developed, focusing on the precise delineation of OARs.

METHODS

The RU-Net architecture enhances feature extraction through embedded residual in residual dence block (RRDB) modules. It was trained and evaluated using a multiplanar (axial, coronal, and sagittal) MRI dataset. The study benchmarks RU-Net against pyramid scene parsing network, residual U-Net, U-Net++, and traditional U-Net models. Performance was assessed using categorical cross-entropy and dice loss metrics.

RESULTS

Under the dice loss function, RU-Net consistently outperformed competing models across all anatomical planes. It achieved superior scores in accuracy, precision, recall, and intersection over union (IOU), particularly excelling in class-wise IOUs for critical structures such as bladder, rectum, sigmoid colon, and femoral heads.

DISCUSSION: The architecture improved both boundary delineation and overall prediction quality. It is established that the proposed RU-Net shows its supremacy under dice loss conditions in terms of proper prediction and segmentation.

CONCLUSION

RU-Net offers a robust solution for multiorgan segmentation in gynecological BT. Its advanced design effectively addresses anatomical distortions, demonstrating strong potential to improve clinical outcomes in magnetic resonance-guided treatment planning.