Improving brain tumor segmentation with anatomical prior-informed pre-training

Frontiers in Medicine

Medicine

Zeyang Li¹  Zhijian Song²  Haoran Wang²  Kang Wang²  Shuo Wang²  Siyu Liu²  Manning Wang²  Mingyuan Pan³ 

[1]Department of Neurosurgery, Zhongshan Hospital, Fudan University, Shanghai, China

[2]Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China

[3]Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai, China

[4]Radiation Oncology Center, Huashan Hospital, Fudan University, Shanghai, China

关键词: masked autoencoder;    anatomical priors;    transformer;    brain tumor segmentation;    magnetic resonance image;    self-supervised learning;   

DOI  :  10.3389/fmed.2023.1211800

 received in 2023-04-25, accepted in 2023-08-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionPrecise delineation of glioblastoma in multi-parameter magnetic resonance images is pivotal for neurosurgery and subsequent treatment monitoring. Transformer models have shown promise in brain tumor segmentation, but their efficacy heavily depends on a substantial amount of annotated data. To address the scarcity of annotated data and improve model robustness, self-supervised learning methods using masked autoencoders have been devised. Nevertheless, these methods have not incorporated the anatomical priors of brain structures.MethodsThis study proposed an anatomical prior-informed masking strategy to enhance the pre-training of masked autoencoders, which combines data-driven reconstruction with anatomical knowledge. We investigate the likelihood of tumor presence in various brain structures, and this information is then utilized to guide the masking procedure.ResultsCompared with random masking, our method enables the pre-training to concentrate on regions that are more pertinent to downstream segmentation. Experiments conducted on the BraTS21 dataset demonstrate that our proposed method surpasses the performance of state-of-the-art self-supervised learning techniques. It enhances brain tumor segmentation in terms of both accuracy and data efficiency.DiscussionTailored mechanisms designed to extract valuable information from extensive data could enhance computational efficiency and performance, resulting in increased precision. It's still promising to integrate anatomical priors and vision approaches.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Li, Wang, Liu, Pan, Wang, Wang and Song.