Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

TL;DR

This paper introduces a self-supervised deep learning method for reconstructing skull defects in post-operative brain CT images, aiding in surgical planning and research without requiring annotated data.

Contribution

It presents a novel self-supervised CNN approach for skull reconstruction that outperforms manual methods and does not need annotated decompressive craniectomy images.

Findings

Outperforms current manual reconstruction methods

Effective in cases with large skull defects

Works with real and simulated post-operative CT images

Abstract

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.