Subcortical brain segmentation with convolutional neural networks

Zhaoxuan Gong; Xianyu Chen; Wei Guo; Ronghui Ju; Wenjun Tan; Wei Zhou; Guodong Zhang

doi:10.1117/12.2643354

12 October 2022 Subcortical brain segmentation with convolutional neural networks

Zhaoxuan Gong, Xianyu Chen, Wei Guo, Ronghui Ju, Wenjun Tan, Wei Zhou, Guodong Zhang

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421B (2022) https://doi.org/10.1117/12.2643354
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Subcortical brain segmentation is a challenging task due to the anatomical variability in both shape and size between patients, such as thalamus, hippocampus and amygdala. It requires the accurate segmentation of these structures to measure their volume and surface. However, few methods can obtain accurate segmentation because the boundaries of these structures are obscure in MR images. We propose an attention-based convolutional neural network for subcortical brain segmentation. In our method, image clipping is firstly applied for pre-processing. Accurate subcortical brain segmentation is obtained by using attention-based convolutional neural network. Maximum connectivity is finally applied for post-processing. Experimental results in 35 subjects showed that the proposed method segment the brain region with higher accuracy than other methods. The Dice, TPR and VD measures show that the proposed method is able to provide a precise and robust segmentation estimate. The proposed method is a suitable alternative to assist the manual subcortical brain segmentation task.

Citation Download Citation

Zhaoxuan Gong, Xianyu Chen, Wei Guo, Ronghui Ju, Wenjun Tan, Wei Zhou, and Guodong Zhang "Subcortical brain segmentation with convolutional neural networks", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421B (12 October 2022); https://doi.org/10.1117/12.2643354

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KEYWORDS

Image segmentation

Brain

Thalamus

Neuroimaging

Convolutional neural networks

Magnetic resonance imaging

Amygdala

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