  Decompressive craniectomy (DC) is a common surgical procedure consisting of
the removal of a portion of the skull that is performed after incidents such as
stroke, traumatic brain injury (TBI) or other events that could result in acute
subdural hemorrhage and/or increasing intracranial pressure. In these cases, CT
scans are obtained to diagnose and assess injuries, or guide a certain therapy
and intervention.
  We propose a deep learning based method to reconstruct the skull defect
removed during DC performed after TBI from post-operative CT images. This
reconstruction is useful in multiple scenarios, e.g. to support the creation of
cranioplasty plates, accurate measurements of bone flap volume and total
intracranial volume, important for studies that aim to relate later atrophy to
patient outcome. We propose and compare alternative self-supervised methods
where an encoder-decoder convolutional neural network (CNN) estimates the
missing bone flap on post-operative CTs. The self-supervised learning strategy
only requires images with complete skulls and avoids the need for annotated DC
images. For evaluation, we employ real and simulated images with DC, comparing
the results with other state-of-the-art approaches. The experiments show that
the proposed model outperforms current manual methods, enabling reconstruction
even in highly challenging cases where big skull defects have been removed
during surgery.
