Purpose: Accurate delineation of the urethra is a prerequisite for urethral dose reduction in prostate radiotherapy. However, even in magnetic resonance-guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT-based prostatic urethra segmentation framework.
Methods: Twenty-eight prostate cancer patients were included in this study. In-house 3D half fourier single-shot turbo spin-echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley-free urethra on a 0.35 T MRgRT system. The segmentation pipeline uses 3D nnU-Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM-Conv) layer and spatial recurrent convolution layer (RCL) into nnU-Net. A novel slice-by-slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post-process the segmentation results.
Results: The experimental results show that nnU-Net trained using a combination of Dice, cross-entropy and RD achieved a Dice score of 77.1 ± 2.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95 ± 0.17 mm and 1.84 ± 0.11 mm, respectively. LSTM-Conv and RCL layers only minimally improved the segmentation precision.
Conclusion: We present the first Foley-free MRgRT-based automated urethra segmentation study. Our method is built on a data-driven neural network with novel cost functions and a post-processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy.
Keywords: 3D nnU-Net; LSTM; post-smoothing; radial distance loss; recurrent neural network; urethra segmentation.
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