Machine Segmentation of Pelvic Anatomy in MRI-Assisted Radiosurgery (MARS) for Prostate Cancer Brachytherapy

Purpose: To investigate machine segmentation of pelvic anatomy in magnetic resonance imaging (MRI)-assisted radiosurgery (MARS) for prostate cancer using prostate brachytherapy MRIs acquired with different pulse sequences and image contrasts.

Methods and materials: Two hundred 3-dimensional (3D) preimplant and postimplant prostate brachytherapy MRI scans were acquired with a T2-weighted sequence, a T2/T1-weighted sequence, or a T1-weighted sequence. One hundred twenty deep machine learning models were trained to segment the prostate, seminal vesicles, external urinary sphincter, rectum, and bladder using the MRI scans acquired with T2-weighted and T2/T1-weighted image contrast. The deep machine learning models consisted of 18 fully convolutional networks (FCNs) with different convolutional encoders. Both 2-dimensional and 3D U-Net FCNs were constructed for comparison. Six objective functions were investigated: cross-entropy, Jaccard distance, focal loss, and 3 variations of Tversky distance. The performance of the models was compared using similarity metrics, including pixel accuracy, Jaccard index, Dice similarity coefficient (DSC), 95% Hausdorff distance, relative volume difference, Matthews correlation coefficient, precision, recall, and average symmetrical surface distance. We selected the highest-performing architecture and investigated how the amount of training data, use of skip connections, and data augmentation affected segmentation performance. In addition, we investigated whether segmentation on T1-weighted MRI was possible with FCNs trained on only T2-weighted and T2/T1-weighted image contrast.

Results: Overall, an FCN with a DenseNet201 encoder trained via cross-entropy minimization yielded the highest combined segmentation performance. For the 53 3D test MRI scans acquired with T2-weighted or T2/T1-weighted image contrast, the DSCs of the prostate, external urinary sphincter, seminal vesicles, rectum, and bladder were 0.90 ± 0.04, 0.70 ± 0.15, 0.80 ± 0.12, 0.91 ± 0.06, and 0.96 ± 0.04, respectively, after model fine-tuning. For the 5 T1-weighted images, the DSCs of these organs were 0.82 ± 0.07, 0.17 ± 0.15, 0.46 ± 0.21, 0.87 ± 0.06, and 0.88 ± 0.05, respectively.

Conclusions: Machine segmentation of the prostate and surrounding anatomy on 3D MRIs acquired with different pulse sequences for MARS low-dose-rate prostate brachytherapy is possible with a single FCN.