Combined Metaheuristic Algorithm and Radiomics Strategy for the Analysis of Neuroanatomical Structures in Schizophrenia and Schizoaffective Disorders - 23/09/21
, G. Kavitha Bienvenue sur EM-consulte, la référence des professionnels de santé.
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| pages | 16 |
| Iconographies | 15 |
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| Autres | 0 |
Graphical abstract |
Highlights |
• | Shape prior level set effectively segments the brain regions from psychotic MR images. |
• | Radiomic features significantly capture the shape and pattern variations from regions. |
• | The BPSO-FSVM classifier identifies the relevant features and discriminate psychotic subjects. |
• | Cerebellum region shows significant variations and used as a biomarker. |
Abstract |
Objectives |
Schizophrenia (SZ) is the most chronic disabling psychotic brain disorder. It is characterized by delusions and auditory hallucinations, as well as impairments in memory. Schizoaffective (SA) signs are co-morbid with SZ and are characterized by symptoms of SZ and mood disorder. Various researches suggest that SZ and SA share a number of equally severe cognitive deficits, but the pathophysiology has not yet been addressed in a comprehensive way. In this work, the heterogeneity in whole brain, ventricle and cerebellum region from psychotic MR brain images is examined using Machine learning and radiomic features.
Materials and methods |
T1 weighted MR brain images are obtained from Schizconnect database for the analysis. The shape prior level set method is used to segment the ventricle and cerebellum structures. The radiomic features which include shape and texture are extracted from these regions to discriminate the SZ and SA subjects. The performance of these features is evaluated with Binary Particle Swarm Optimization (BPSO) based Fuzzy Support Vector Machine (FSVM) classifier.
Results |
The shape constrained Level Set method is able to better segment ventricles and cerebellum regions from the images. The significant features that are extracted from whole brain, ventricle and cerebellum are identified by the BPSO based FSVM. The combination of radiomic features extracted from cerebellum region achieved high classification accuracy (90.09%) using metaheuristic algorithm. The extracted features from cerebellum are correlated with PANSS score. The causal analysis shows that there is an association been the tissue texture variation in identifying the disease severity. The symmetry analysis shows that left brain mean area is larger than the right side area. In particular SA has low cerebellum area compared to SZ. The radiomic features such as Hermite, Laws and tensor extracted from the left cerebellum show a significant texture variation in all the considered subjects (p<0.0001).
Conclusions |
The results are clinically relevant in discriminating the pattern change in the structure, hence this biomarker and frame work could be used for the severity study of psychotic disorders.
Le texte complet de cet article est disponible en PDF.Keywords : Schizophrenia, Schizoaffective, Radiomic, Metaheuristic, Classifier
Plan
Vol 42 - N° 5
P. 353-368 - octobre 2021 Retour au numéro
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