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

Abstract

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.

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

“…Usinskas et al [ 50 ] presented an unsupervised method to segment ischemic stroke regions based on computing mean and standard deviation features. Several automated methods have been published for segmenting infarcts in MRI images [ 51 , 52 , 53 , 54 , 55 ]. Li et al [ 56 ] reported an unsupervised method based on multistage processes that included tensor field calculation, diffusion anisotropy measurement, adaptive multiscale statistical classification for segmentation of infarct volume, and partial volume voxel re-classification.…”

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

“…The ATLAS v2.0 dataset was developed using similar protocols and methods as the ATLAS v1.2 dataset, which has been successfully utilized to develop numerous lesion segmentation methods for the last several years [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] . For ATLAS v2.0, detailed manual quality control for image quality occurred during the initial lesion segmentation, and all segmentations were examined for quality by two additional researchers.…”

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

“…1 ATLAS v1.2 has been accessed and cited widely since its release in 2018, with reports including the improved performance of stroke lesion segmentation algorithms using novel methods, particularly deep learning and convolutional neural networks (e.g. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] ).…”

“…The ATLAS v2.0 dataset was developed using similar protocols and methods as the ATLAS v1.2 dataset, which has been successfully utilized to develop numerous lesion segmentation methods for the last several years. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] For ATLAS v2.0, detailed manual quality control for image quality occurred during the initial lesion segmentation, and all segmentations were examined for quality by two additional researchers. Following preprocessing, lesions were again checked for proper registration to template space.…”