Wang Kang, Li Zeyang, Wang Haoran, Liu Siyu, Pan Mingyuan, Wang Manning, Wang Shuo, Song Zhijian
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai, China.
Front Med (Lausanne). 2023 Sep 13;10:1211800. doi: 10.3389/fmed.2023.1211800. eCollection 2023.
Precise 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.
This 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.
Compared 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.
Tailored 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.
在多参数磁共振图像中精确描绘胶质母细胞瘤对于神经外科手术及后续治疗监测至关重要。Transformer模型在脑肿瘤分割方面已显示出潜力，但其有效性在很大程度上依赖于大量的标注数据。为了解决标注数据稀缺的问题并提高模型的鲁棒性，已设计出使用掩码自动编码器的自监督学习方法。然而，这些方法尚未纳入脑结构的解剖学先验知识。
本研究提出了一种基于解剖学先验知识的掩码策略，以增强掩码自动编码器的预训练，该策略将数据驱动的重建与解剖学知识相结合。我们研究了肿瘤在各种脑结构中存在的可能性，然后利用这些信息来指导掩码过程。
与随机掩码相比，我们的方法使预训练能够专注于与下游分割更相关的区域。在BraTS21数据集上进行的实验表明，我们提出的方法优于当前最先进的自监督学习技术的性能。它在准确性和数据效率方面都增强了脑肿瘤分割。
旨在从大量数据中提取有价值信息的定制机制可以提高计算效率和性能，从而提高精度。整合解剖学先验知识和视觉方法仍然很有前景。
