Neurocomputing, volume 575, pages 127315

Neighbouring-slice Guided Multi-View Framework for brain image segmentation

Xuemeng Hu 1
Zhongyu Li 1
Zhong Yu Li 1
Yi Wu 1
Yi Wu 1
Jingyi Liu 1
Xiliang Luo 1
Xiang Luo 1
Jing Ren 2
Show full list: 9 authors
2
 
Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
Publication typeJournal Article
Publication date2024-03-01
Journal: Neurocomputing
Q1
Q1
SJR1.815
CiteScore13.1
Impact factor5.5
ISSN09252312, 18728286
Computer Science Applications
Artificial Intelligence
Cognitive Neuroscience
Abstract
Automated segmentation of brain images is a critical task in neuroscience for brain registration, atlas generation, etc. Deep learning techniques have been widely investigated for segmenting brain images, where most existing methods only consider each brain slice independently in a single view, limiting their ability to explore the correlation among adjacent slices and spatial brain structures. This paper proposes a Neighbouring-slice Guided Multi-view Framework for automated segmentation of brain images. To fully utilize the information between neighbouring slices, we design a dual-decoder network to segment targets (e.g., regions/tumors) in brain images and the edge of each brain targets simultaneously, by calculating the difference between adjacent slices. Considering the fact that some brain images cannot be fully recognized in a single view, we integrate the neighbouring-slice strategy in a multi-view segmentation framework to fully explore spatial structures in 3D brains. The proposed framework is validated on the automated segmentation of diverse brain tumors and brain regions including CTX (cerebellar cortex), CP (caudoputamen), HPF (hippocampal formation), BS (brain stem), CB (cerebellum), and CBX (cerebellar cortex), in LSFM (Light-sheet Fluorescence Microscopy) and MRI (Magnetic Resonance Imaging) modalities, demonstrating superior performance in comparison with state-of-the-arts. The codes are released at: https://github.com/NeuronXJTU/NGMV.

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