School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Course of Science and Technology, Graduate School of Science and Technology, Tokai University, Tokyo 108-8619, Japan.
Sensors (Basel). 2021 Mar 10;21(6):1952. doi: 10.3390/s21061952.
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.
自动化分割方法对于减少脑梗死的残疾和死亡风险至关重要，可实现早期检测、及时采取行动和立即治疗。本文旨在开发一种全自动方法，从 T1 加权脑扫描中分割梗死病变。作为一项关键创新，所提出的方法结合了变分模态分解和基于深度学习的分割，以利用这两种方法的优势，提供更好的结果。本文有三个主要技术贡献。首先，应用变分模态分解作为预处理，将梗死病变与不需要的非梗死组织区分开来。其次，提出了重叠补丁策略，以减少基于深度学习的分割任务的工作量。最后，开发了一个三维 U-Net 模型，用于进行梗死病变的逐块分割。利用来自公共数据集的 239 个脑扫描来开发和评估所提出的方法。实验结果表明，所提出的自动分割方法具有良好的性能，平均骰子相似系数（DSC）为 0.6684，交并比（IoU）为 0.5022，平均对称面距离（ASSD）为 0.3932。
