The brain is significantly deformed during neurosurgery, in particular because of
the removal of tumor tissue. Because of this deformation, intraoperative data is needed
for accurate navigation in image-guided surgery. During the surgery, it is easier
to acquire ultrasound images than Magnetic Resonance (MR) images. However, ultrasound
images are difficult to interpret. Several methods have been developed to register
preoperative MR and intraoperative ultrasound images, to allow accurate navigation
during neurosurgery. Model-based methods need the location of the resection cavity
to take into account the tissue removal in the model. Manually segmenting this cavity
is extremely time consuming and cannot be performed in the operating room. It is also
difficult and error-prone because of the noise and reconstruction artifacts in the
ultrasound images. In this work, we present a method to perform the segmentation of
the resection cavity automatically. We manually labelled the resection cavity on the
ultrasound volumes from a database of 23 patients. We trained a Unet-based artificial
neural network with our manual segmentation and evaluated several variations of the
method. Our best method results in 0.86 mean Dice score over the 10 testing cases.
The Dice scores range from 0.68 to 0.96, and nine out of ten are higher than 0.75.
For the most difficult test cases, lacking clear contour, the manual segmentation
is also difficult but our method still yields acceptable results. Overall the segmentations
obtained with the automatic methods are qualitatively similar to the manual ones.
