  Effective transperineal ultrasound image guidance in prostate external beam
radiotherapy requires consistent alignment between probe and prostate at each
session during patient set-up. Probe placement and ultrasound image
inter-pretation are manual tasks contingent upon operator skill, leading to
interoperator uncertainties that degrade radiotherapy precision. We demonstrate
a method for ensuring accurate probe placement through joint classification of
images and probe position data. Using a multi-input multi-task algorithm,
spatial coordinate data from an optically tracked ultrasound probe is combined
with an image clas-sifier using a recurrent neural network to generate two sets
of predictions in real-time. The first set identifies relevant prostate anatomy
visible in the field of view using the classes: outside prostate, prostate
periphery, prostate centre. The second set recommends a probe angular
adjustment to achieve alignment between the probe and prostate centre with the
classes: move left, move right, stop. The algo-rithm was trained and tested on
9,743 clinical images from 61 treatment sessions across 32 patients. We
evaluated classification accuracy against class labels de-rived from three
experienced observers at 2/3 and 3/3 agreement thresholds. For images with
unanimous consensus between observers, anatomical classification accuracy was
97.2% and probe adjustment accuracy was 94.9%. The algorithm identified optimal
probe alignment within a mean (standard deviation) range of 3.7$^{\circ}$
(1.2$^{\circ}$) from angle labels with full observer consensus, comparable to
the 2.8$^{\circ}$ (2.6$^{\circ}$) mean interobserver range. We propose such an
algorithm could assist ra-diotherapy practitioners with limited experience of
ultrasound image interpreta-tion by providing effective real-time feedback
during patient set-up.
