Presults for the aI-Brachy study : Utilizing deep learning for needle reconstruction in prostate brachytherapy

• Purpose To develop a deep neural network for automatic reconstruction of needles in ultrasound images depicting the prostate during brachytherapy treatment of prostate cancer.Methods Ultrasound tomographies of the prostate from 907 treatments were used to train the artificial intelligent (AI) algorithm. The image matrices were downsampled to 128x128x128 and were used as in-data when training the AI, a 27 layer convolutional neural network. The needles were identified manually by medical physicists using conventional software. These reconstructions were used as gold standard when training the algorithm. An additional set of examinations were used for validation where the needle reconstructions by the AI were compared to the manual reconstructions. The root mean square deviation (RMSD) of needle position, including the central part (70 slices) of the needle was measured in order to avoid influence from artefacts around the needle tip. The result was also evaluated through visual inspection (see image). The times spent for manual vs. AI reconstruction were compared.Results RMSD for manual vs. AI reconstruction is on average (n=170) 1.18±1.0mm, whereas the difference between two manual operators is 0.02±0.06mm, which suggests that the AI is inferior to manual operators. The visual inspection, however, shows AI to be very accurate in positioning the needles. Manual reconstruction took approximately 11.0 minutes, whereas the time for the trained AI is negligible in comparison. Worth noticing regarding RMSD calculations is that, due to limited image resolution, small values may be under-estimated, hence overestimating the difference between the reconstruction methods.Conclusions The study implies that an AI may reconstruct needles for brachytherapy treatments of prostate cancer. The larger deviations between AI algorithm and manual operators, compared to between human operators appears to disagree with the high accuracy of the visual evaluation. However, visually, manual needle reconstructions appear to deviate more from the ultrasound image than do the AI reconstructions. This discrepancy is mainly caused by manual reconstruction software assuming straight needles, unlike the AI. We conclude that AI gives the opportunity to save a substantial amount of treatment planning time, when the patient is anesthetised. Further studies are needed to determine whether different reconstruction methods impact treatment plans.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

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