PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net

• Autorzy: Chandra Anjali , Verma Shrish , Raghuvanshi A.S. , Kuber Bodhey Narendra

Identyfikatory

DOI

10.1016/j.bbe.2023.02.003

• Języki publikacji: EN

Abstrakty

EN

Background: The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differential diagnosis in neurodegenerative diseases such as Autism, Alzheimer’s disease (AD), and more. Manual segmentation and parcellation of Cc are laborious and time-consuming. The present work proposes a novel work of automated parcellated Cc (PCc) segmentation that will serve as a potential biomarker to study and diagnose neurological disorders in brain MRI images. Method: In this perspective, the present work aims to develop an automated PCc segmentation from mid-sagittal T1- weighted (w) 2D brain MRI images using a deep learning-based fully convolutional network, a modified residual attention U-Net, referred to as PCcS-RAU-Net. The model has been modified to use a multi-class segmentation configuration with five target classes (parcels): rostrum, genu, mid-body, isthmus and splenium. Results: The experimental research uses two benchmark MRI datasets, ABIDE and OASIS. The proposed PCcS-RAU-Net outperformed existing methods on the ABIDE dataset with a DSC of 97.10% and MIoU of 94.43%. Furthermore, the model’s performance is validated on the OASIS and Real clinical image (RCI) data and hence verifies the model’s generalization capability. Conclusion: The proposed PCcS-RAU-Net model extracts essential characteristics such as the total area of the Cc (TCcA) to categorize MRI slices into healthy controls (HC) and disease groups. Also, sub-regional areas, Cc1A to Cc5A, help study atrophy progression for early diagnosis.

Słowa kluczowe

EN

parcellated Corpus callosum; image segmentation; deep learning; neurodegenerative disease

PL

ciało modzelowate; segmentacja obrazu; uczenie głębokie; choroba neurodegeneracyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 2
• Strony: 403--427
• Opis fizyczny: Bibliogr. 55 poz., rys., tab.

Twórcy

autor

Chandra Anjali

• Department of Electronics and Communication Engineering, NIT Raipur, Chhattisgarh, India

autor

Verma Shrish

• Department of Electronics & Communication Engineering, NIT Raipur, C.G, India

autor

Raghuvanshi A.S.

• Department of Electronics & Communication Engineering, NIT Raipur, C.G, India

autor

Kuber Bodhey Narendra

• Department of Radiodiagnosis, AIIMS Raipur, C.G, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)