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.