Accurate neuroanatomy segmentation using 3D spatial and anatomical attention neural networks

Hewei Cheng; Zhengyu Ren; Peiyang Li; Yin Tian; Wei Wang; Zhangyong Li; Yong Fan

doi:10.1117/12.2644416

12 October 2022 Accurate neuroanatomy segmentation using 3D spatial and anatomical attention neural networks

Hewei Cheng, Zhengyu Ren, Peiyang Li, Yin Tian, Wei Wang, Zhangyong Li, Yong Fan

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123422O (2022) https://doi.org/10.1117/12.2644416
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Brain structure segmentation from 3D magnetic resonance (MR) images is a prerequisite for quantifying brain morphology. Since typical 3D whole brain deep learning models demand large GPU memory, 3D image patch-based deep learning methods are favored for their GPU memory efficiency. However, existing 3D image patch-based methods are not well equipped to capture spatial and anatomical contextual information that is necessary for accurate brain structure segmentation. To overcome this limitation, we develop a spatial and anatomical context-aware network to integrate spatial and anatomical contextual information for accurate brain structure segmentation from MR images. Particularly, a spatial attention block is adopted to encode spatial context information of the 3D patches, an anatomical attention block is adopted to aggregate image information across channels of the 3D patches, and finally the spatial and anatomical attention blocks are adaptively fused by an element-wise convolution operation. Moreover, an online patch sampling strategy is utilized to train a deep neural network with all available patches of the training MR images, facilitating accurate segmentation of brain structures. Ablation and comparison results have demonstrated that our method is capable of achieving promising segmentation performance, better than state-of-the-art alternative methods by 3.30% in terms of Dice scores.

Citation Download Citation

Hewei Cheng, Zhengyu Ren, Peiyang Li, Yin Tian, Wei Wang, Zhangyong Li, and Yong Fan "Accurate neuroanatomy segmentation using 3D spatial and anatomical attention neural networks", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123422O (12 October 2022); https://doi.org/10.1117/12.2644416

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KEYWORDS

Image segmentation

Brain

Neuroimaging

Convolution

Magnetic resonance imaging

3D image processing

3D modeling

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