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

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.

[1]  Heinz Handels,et al.  Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers , 2014, Medical Imaging.

[2]  Joseph C. Griffis,et al.  Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans , 2016, Journal of Neuroscience Methods.

[3]  Hairong Zheng,et al.  CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke , 2019, MICCAI.

[4]  Huseyin Oztoprak,et al.  Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning , 2019, Comput. Intell. Neurosci..

[5]  Dominique Zosso,et al.  Two-Dimensional Variational Mode Decomposition , 2015, EMMCVPR.

[6]  J A Fiez,et al.  Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability , 2000, Human brain mapping.

[7]  Lukasz Kurgan,et al.  Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease , 2020, Medical Image Anal..

[8]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[9]  Saeed Hassanpour,et al.  Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network , 2019, NeuroImage: Clinical.

[10]  S. Lahmiri.  Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance , 2016 .

[11]  Olivier Salvado,et al.  Lesion segmentation from multimodal MRI using random forest following ischemic stroke , 2014, NeuroImage.

[12]  Irina Voiculescu,et al.  Family of boundary overlap metrics for the evaluation of medical image segmentation , 2018, Journal of medical imaging.

[13]  Lei Ai,et al.  A large, open source dataset of stroke anatomical brain images and manual lesion segmentations , 2017, Scientific Data.

[14]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[15]  Yong Xia,et al.  D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Wan Nurshazwani,et al.  Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images , 2019 .

[17]  Jeffrey R. Binder,et al.  A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images , 2019, NeuroImage: Clinical.

[18]  V. Feigin,et al.  Global Stroke Statistics 2019 , 2020, International journal of stroke : official journal of the International Stroke Society.

[19]  U. Rajendra Acharya,et al.  Automatic detection of ischemic stroke using higher order spectra features in brain MRI images , 2019, Cognitive Systems Research.

[20]  Sukanta Sabut,et al.  Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier , 2019, Int. J. Comput. Intell. Appl..

[21]  Qiegen Liu,et al.  X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies , 2019, MICCAI.

[22]  P. Gorelick,et al.  The global burden of stroke: persistent and disabling , 2019, The Lancet Neurology.

[23]  Nils Daniel Forkert,et al.  Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study , 2015, PloS one.

[24]  S. M. Riazul Islam,et al.  A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion , 2020, Inf. Fusion.

[25]  Pamela W. Duncan,et al.  Disability Measures in Stroke: Relationship Among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale , 2004, Stroke.

[26]  S. K. Sahari,et al.  A Review of MRI Acute Ischemic Stroke Lesion Segmentation , 2020 .

[27]  Anjan Gudigar,et al.  Automated Categorization of Multi-Class Brain Abnormalities Using Decomposition Techniques With MRI Images: A Comparative Study , 2019, IEEE Access.

[28]  Brian Avants,et al.  Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis , 2016, Human brain mapping.