ESTRO 2023 - Abstract Book

S643

Monday 15 May 2023

ESTRO 2023

Results Table 1 reports the average γ -pass rates for the two performed experiments with runtimes reported for the patient case. The B-LSTM predictions demonstrate good agreement with the MC simulations and comparable results to the deterministic networks. As illustrated in Figure 2, the B-LSTM correctly identifies the areas of high uncertainty, i.e., σ correlates well with the location of voxels failing the γ -criterion. The σ prediction performed best on the phantom dataset, but was less accurate on the patient lung dataset, partially due to interpolation artifacts. The percentage of voxels significantly deviating ( ≥ 3 σ ) was 0.2% for the phantom case and 5.56% for the patient case.

Phantom Patient y-pass rate mean (%) y-pass rate mean (%) Runtime (ms)*

Neishabouri LSTM

96.00

99.60

9.8

Deterministic LSTM 97.81

99.70

8.0

B-LSTM (Ensemble size = 100)

97.93

99.59

67**

* using Nvidia RTX A5000 GPU ** 57.6 ms for prediction ensemble + 9.4 ms for mean & variance