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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 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.
Highlights
Working with deep learning models can guarantee better performance, but they considerably demand a high number of hyperparameters
Note that all of these measurements were calculated after age patches using 3D U-Net were stitched together again, using the reference patch posithe postprocessing stage; that is, they were not calculated for patch-wise segmentation but tion numbers and 3D connected component labeling
Coefficient (DSC)Note that all of these measurements were calculated assessment after Similar the postprocessing that is, they were not calculated for patch-wise segmentato intersection over union (IoU), dice similarity coefficient (DSC) stage; measures the overlapped between the segmented lesion and the lesion mask
Summary
Generally known as stroke, is a global health issue and public health priority. It is a significant cause of disability and the second leading cause of death worldwide [1]. Examples of disabilities may include transient or lasting paralysis on one or both sides of the body, difficulties in speaking or eating, and muscular coordination loss. Such devastating and life-altering results after brain infarction can seriously impact a critical economic and humanistic burden as well [1]. An annual $51.2 billion economic loss results from stroke-reducing approaches, for example, medical costs and costs for rehabilitation in poststroke patients such as physical functioning and caregiver involvement [4]
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