Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors

Chuchart Pintavirooj¹  MayPhu Paing¹  Supan Tungjitkusolmun¹  Sarinporn Visitsattapongse¹  ToanHuy Bui² 

[1] School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan;

关键词: brain infarction;    stroke;    U-Net;    variational mode decomposition;   

DOI  :  10.3390/s21061952

来源: DOAJ

【 摘 要 】

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 are 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.

【 授权许可】

Unknown