Sub-acute Ischemic Stroke Lesion Research Articles

Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease

Ischemic stroke lesion and white matter hyperintensity (WMH) lesion appear as regions of abnormally signal intensity on magnetic resonance image (MRI) sequences. Ischemic stroke is a frequent cause of death and disability, while WMH is a risk factor for stroke. Accurate segmentation and quantification of ischemic stroke and WMH lesions are important for diagnosis and prognosis. However, radiologists have a difficult time distinguishing these two types of similar lesions. A novel deep residual attention convolutional neural network (DRANet) is proposed to accurately and simultaneously segment and quantify ischemic stroke and WMH lesions in the MRI images. DRANet inherits the advantages of the U-net design and applies a novel attention module that extracts high-quality features from the input images. Moreover, the Dice loss function is used to train DRANet to address data imbalance in the training data set. DRANet is trained and evaluated on 742 2D MRI images which are produced from the sub-acute ischemic stroke lesion segmentation (SISS) challenge. Empirical tests demonstrate that DRANet outperforms several other state-of-the-art segmentation methods. It accurately segments and quantifies both ischemic stroke lesion and WMH. Ablation experiments reveal that attention modules improve the predictive performance of DRANet.

Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI

Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normalization, followed by next three layers of rectified linear unit (ReLU). Eleventh layer is average pooling 2D which is followed by fully connected (FC), softmax and classification layers respectively. The presented method is verified on six MICCAI databases namely multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016, 2017 and sub-acute ischemic stroke lesion segmentation (SISS-ISLES) 2015. The computational time is also measured across each benchmark dataset such as 53 s on BRATS 2013, 26 s on BRATS 2014, 41 s on BRATS 2015, 36 s on BRATS 2016, and 38 s on BRATS 2017 and 4.13 s on ISLES 2015 proving that the proposed technique has less processing time. The proposed method achieved 0.9943 ACC, 1.00 SP, 0.9839 SE on BRATS 2013, 0.9538 ACC, 0.9991 SP, 0.7196 SE on BRATS 2014, 0.9978 ACC, 1.00 SP, 0.9919 SE on BRATS 2015, 0.9569 ACC, 0.9491 SP, 0.9755 SE on BRAST 2016, 0.9778 ACC, 0.9770 SP, 0.9789 SE on BRATS 2017 and 0.9227 ACC, 1.00 SP, 0.8814 SP on ISLES 2015 datasets respectively.

Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation

As manual delineation of lesions in medical image is a very tedious and time consuming process, accurate and automatic segmentation of medical images can assist diagnosis and treatment. In this study, we propose a deep convolution neural network for stroke magnetic resonance imaging(MRI) segmentation. The main structure of our network consists of two symmetrical deep sub-networks, in which dense blocks are embedded for extracting effective features from sparse pixels to alleviate the over-fitting problem of deep networks. We use the multi-kernel to divide the network into two sub-networks for acquiring more receptive fields, and the dropout regularization method to achieve an effective feature mapping. For the post-processing of the soft segmentation, we use image median filtering to alleviate noises and preserve the edge details of images. Our network is evaluated on two public benchmark segmentation challenges (SISS: sub-acute ischemic stroke lesion segmentation and SPES: acute stroke outcome/penumbra estimation) with multi-modality MRI sequences. According to the results of the public benchmark reports, among 9 teams participating in both SISS and SPES challenges at the same time, our network achieves the top performance on SISS challenge, and the top 3 performance on the SPES challenge. In addition, our network also exhibits state-of-the-art performance compared with other segmentation methods. Finally, we extensively evaluate our network with an ablation experiment. The experimental results show that both multi-kernel and dropout strategies can improve the segmentation accuracy of our proposed network.

An Effective Approach for Sub-acute Ischemic Stroke Lesion Segmentation by Adopting Meta-Heuristics Feature Selection Technique Along with Hybrid Naive Bayes and Sample-Weighted Random Forest Classification

In Earth, the most crucial illness for cause of death is ischemic stroke. Ischemic stroke arises as a result of an obstacle within a blood vessel supplying blood to the brain. In this paper, for sub-acute ischemic stroke lesion segmentation, we utilize an effective meta-heuristic feature selection technique along with hybrid Naive Bayes (NB) and sample weighted random forest (SWRF) classification approach. Initially, the features are extracted from the pre-processed image, after that, the feature selection is done by using the multi-objective enhanced firefly algorithm. To improve the classification performance, the dimensionality of the feature vectors and errors are reduced by eliminating such irrelevant and redundant features. After the feature selection process, an ensemble of NB and SWRF classifiers is used for segmenting the image. Here the NB classifier is trained and applied to estimate the weights of training samples. Then, the training samples with estimated weights are utilized to train SWRF. In our work stroke lesion segmentation is formulated as a binary classification problem where every local region is classified as either affected or non-affected area.

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.

The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.

Segmentation of ischemic stroke lesion from 3d mr images using random forest

This paper focuses on segmentation of ischemic stroke lesion from the dataset contributed by Ischemic Stroke Lesion Segmentation (ISLES)-2015 Sub-acute Ischemic Stroke lesion Segmentation (SISS) challenge. The dataset comprises 28 stroke cases of magnetic resonance (MR) images. This study considers fluid attenuation inversion recovery (FLAIR) MR images for the segmentation of lesions. MR images are affected by various artifacts and noise. Hence, we applied wavelet based data denoising technique by optimal parameter selection for stroke lesion enhancement followed by random forest (RF). RF classifier was trained for different part of the brain for segmenting the stroke lesion. The obtained results show best overall segmentation accuracy when compared with the other methods. To measure the image similarity between the ground truth and the segmented image, we used the evaluation method provided by the ISLES-2015. The average symmetric surface distance (ASSD) of the segmentation was measured to be 4.57 mm, while Dice’s coefficient ((DC) lies between 0 and 1) was used to measure the volume overlap accuracy with an average of 0.67. The maximum of all surface distance was given by Hausdorff distance (HD) with an average of 28.09 mm. The average precision and average recall observed was 0.70 and 0.71 respectively. The ISLES image data and ground truth images kept on being openly accessible through an online virtual skeleton database which is an ongoing benchmarking resource.

Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences.

To analyse the relationship between structure and (dys-)function of the brain after stroke, accurate and repeatable segmentation of the lesion area in magnetic resonance (MR) images is required. Manual delineation, the current gold standard, is time consuming and suffers from high intra- and inter-observer differences. A new approach is presented for the automatic and reproducible segmentation of sub-acute ischemic stroke lesions in MR images in the presence of other pathologies. The proposition is based on an Extra Tree forest framework for voxel-wise classification and mainly intensity derived image features are employed. A thorough investigation of multi-spectral variants, which combine the information from multiple MR sequences, finds the fluid attenuated inversion recovery sequence to be both required and sufficient for a good segmentation result. The accuracy can be further improved by adding features extracted from the T1-weighted and the diffusion weighted sequences. The use of other sequences is discouraged, as they impact negatively on the results. Quantitative evaluation was carried out on 37 clinical cases. With a Dice coefficient of 0.65, the method outperforms earlier published methods. The approach proves especially suitable to differentiate between new stroke and other white matter lesions based on the FLAIR sequence alone. This, and the high overlap, renders it suitable for automatic screening of large databases of MR scans, e.g. for a subsequent neuropsychological investigation. Finally, each feature's importance is assessed in detail and the approach's statistical dependency on clinical and image characteristics is investigated.

Corrigendum to “Medical image analysis methods in MR/CT-imaged acute–subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal” [NeuroImage: Clinical 1 (2012) 164–178

[This corrects the article DOI: 10.1016/j.nicl.2012.10.003.].