White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

• Type: Chapter or Part of a Book
• Date: 2019-10

Authors

Tran Anh Tuan (Author) Other; Pham The Bao (Author) Other; Jin Young Kim (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Book

Title: VipIMAGE 2019 - Proceedings of the VII ECCOMAS Thematic = Lecture Notes in Computational Vision and Biomechanics

Pages: 188-195

Publisher: Springer

Indexing

ISI Proceedings

Scopus - 1 Citation

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: P-00R-5FQ

DOI: 10.1007/978-3-030-32040-9_20

Abstract (EN): The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7