  This work presents a preliminary evaluation of the use of the convolutional
neural network nnU-NET to automatically contour the volume of Glioblastoma
Multiforme in medical images of patients. The goal is to assist the preparation
of the Treatment Planning of patients who undergo Boron Neutron Capture Therapy
(BNCT). BNCT is a binary form of radiotherapy based on the selective loading of
a suitable 10-boron concentration into the tumour and on subsequent low-energy
neutron irradiation. The selectivity of the therapeutic effect is based on the
capacity of the boron drug to target preferentially cancer cells, thus
triggering the neutron capture only in the tumour and depositing there a lethal
dose. Even if the tailoring of the beam to the tumour volume is less crucial
for BNCT than for other radiation therapies, a proper delimitation of the
tumour volume is needed to assess a safe and effective dosimetry. In clinical
application the contour must be manually decided by the physician, however, a
tool to automatically define important structures such as the Gross Tumour
Volume (GTV) and the Organs At Risk (OAR) would be beneficial to enable medical
physicists assessing preliminary positioning and simulated dosimetry before the
approval or possible changes introduced by the radiotherapist. Moreover, an
initial contouring may speed up the work of the physician. The nnU-NET was
trained and tested and its performance was evaluated through different
parameters such as the Dice Coefficient. To assess a more meaningful evaluation
for BNCT, for the first time, this work analyzed the difference of the clinical
dosimetry in 16 patients using the manual and the automatic contoured images.
