Automated Morphological Measurements of Brain Structures and Identification of Optimal Surgical Intervention for Chiari I Malformation
| Autor: | Luca Mesin, Forough Mokabberi, Christian Francesco Carlino |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
active contour Computer science Feature extraction Chiari malformation demons 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Health Information Management Chiari I malformation Cerebellum medicine Humans Foramen Magnum Electrical and Electronic Engineering Cerebellar hernia Severe disorder Active contour model Foramen magnum non-rigid registration medicine.diagnostic_test Magnetic resonance imaging Brain Magnetic Resonance Imaging Sagittal plane Computer Science Applications Arnold-Chiari Malformation medicine.anatomical_structure Radiology 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | IEEE journal of biomedical and health informatics. 24(11) |
| ISSN: | 2168-2208 |
| Popis: | The herniation of cerebellum through the foramen magnum may block the normal flow of cerebrospinal fluid determining a severe disorder called Chiari I Malformation (CM-I). Different surgical options are available to help patients, but there is no standard to select the optimal treatment. This paper proposes a fully automated method to select the optimal intervention. It is based on morphological parameters of the brain, posterior fossa and cerebellum, estimated by processing sagittal magnetic resonance images (MRI). The processing algorithm is based on a non-rigid registration by a balanced multi-image generalization of demons method. Moreover, a post-processing based on active contour was used to improve the estimation of cerebellar hernia. This method allowed to delineate the boundaries of the regions of interest with a percentage of agreement with the delineation of an expert of about 85%. Different features characterizing the estimated regions were then extracted and used to develop a classifier to identify the optimal surgical treatment. Classification accuracy on a database of 50 patients was about 92%, with a predictive value of 88% (tested with a leave-one-out approach). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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