Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
| Autor: | Enzo Ferrante, Aneesh Khetani, Ben Glocker, Andrew Stevens, Virginia F. J. Newcombe, Susan Stevenson, Richard Digby, Tom Newman, Franco Matzkin |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Traumatic brain injury business.industry Deep learning medicine.medical_treatment medicine.disease Cranioplasty Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences Skull 0302 clinical medicine medicine.anatomical_structure medicine Decompressive craniectomy Radiology Artificial intelligence business Stroke 030217 neurology & neurosurgery Intracranial pressure |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597122 MICCAI (2) |
| DOI: | 10.1007/978-3-030-59713-9_38 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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