White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Bao, PT (Author) Other; Tuan, TA (Author) Other; Thuy, LL (Author) Other; Kim, JY (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Comput. J.

Vol. 65

Pages: 3081-3090

Indexing

ISI Web of Knowledge - 0 Citations

Clarivate Analytics

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00X-XZY

DOI: 10.1093/comjnl/bxab127

Abstract (EN): According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10