  Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to
produce arrangement of implantable radioactive seeds that deliver a minimum
prescribed dose to the prostate whilst minimizing toxicity to healthy tissues.
There can be multiple seed arrangements that satisfy this dosimetric criterion,
not all deemed 'acceptable' for implant from a physician's perspective. This
leads to plans that are subjective to the physician's/centre's preference,
planning style, and expertise. We propose a method that aims to reduce this
variability by training a model to learn from a large pool of successful
retrospective LDR-PB data (961 patients) and create consistent plans that mimic
the high-quality manual plans. Our model is based on conditional generative
adversarial networks that use a novel loss function for penalizing the model on
spatial constraints of the seeds. An optional optimizer based on a simulated
annealing (SA) algorithm can be used to further fine-tune the plans if
necessary (determined by the treating physician). Performance analysis was
conducted on 150 test cases demonstrating comparable results to that of the
manual prehistorical plans. On average, the clinical target volume covering
100% of the prescribed dose was 98.9% for our method compared to 99.4% for
manual plans. Moreover, using our model, the planning time was significantly
reduced to an average of 2.5 mins/plan with SA, and less than 3 seconds without
SA. Compared to this, manual planning at our centre takes around 20 mins/plan.
