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Book ChapterDOI

Fully Automatic Segmentation for Ischemic Stroke Using CT Perfusion Maps

TL;DR: This work proposes an algorithm for automatic segmentation of ischemic lesion using CT perfusion maps based on encoder-decoder fully convolutional neural network approach and achieves 0.43, 0.53 and 0.45 Dice, precision, and recall respectively on challenge test data set.
Abstract: We propose an algorithm for automatic segmentation of ischemic lesion using CT perfusion maps. Our method is based on encoder-decoder fully convolutional neural network approach. The pre-processing step involves skull stripping and standardization of perfusion maps and extraction of slices with lesions as the training data. These CT perfusion maps are used to train the proposed network for automatic segmentation of stroke lesions. The network is trained by minimizing the weighted combination of cross entropy and dice losses. Our algorithm achieves 0.43, 0.53 and 0.45 Dice, precision, and recall respectively on challenge test data set.
Citations
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Journal ArticleDOI
TL;DR: A novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality and segmentation accuracy and has a potential for improving diagnosis and treatment of the ischemic stroke where access to real DWI scanning is limited.

49 citations


Cites background from "Fully Automatic Segmentation for Is..."

  • ...Recently, deep learning methods have achieved state-of-the-art performance for many medical image segmentation tasks (Shen et al., 2017), and have been applied to ischemic stroke lesion segmentation from CTP images (Pinheiro et al., 2018; Abulnaga and Rubin, 2018; Vikas Kumar Anand et al., 2018)....

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Book ChapterDOI
04 Oct 2020
TL;DR: Zhang et al. as mentioned in this paper proposed a novel cluster-representation learning approach to segment cerebral ischemic infarcted core, which first cluster the training samples based on their similarities of the segmentation difficulty.
Abstract: Computed Tomography Perfusion (CTP) images have drawn extensive attention in acute ischemic stroke assessment due to its imaging speed and ability to provide dynamic perfusion quantification. However, the cerebral ischemic infarcted core has high individual variability and low contrast, and multiple CTP parametric maps need to be referred for precise delineation of the core region. It has thus become a challenging task to develop automatic segmentation algorithms. The widely applied segmentation algorithms such as U-Net lack specific modeling for image subtype in the dataset, and thus the performance remains unsatisfactory. In this paper, we propose a novel cluster-representation learning approach to address these difficulties. Specifically, we first cluster the training samples based on their similarities of the segmentation difficulty. Each cluster represents a different subtype of training images and is then used to train its own cluster-representative model. The models will be capable of extracting cluster-representative features from training samples as clustering priors, which are further fused into an overall segmentation model (for all training samples). The fusion mechanism is able to adaptively select optimal subset(s) of clustering priors which can further guide the segmentation of each unseen testing image and reduce influences from high variability of CTP images. We have applied our method on 94 subjects of ISLES 2018 dataset. By comparing with the baseline U-Net, the experiments have shown an absolute increase of 8% in Dice score and a reduction of 10mm in Hausdorff Distance for ischemic infarcted core segmentation. This method can also be generalized to other U-Net-like architectures to further improve their representative capacity.

3 citations

Book ChapterDOI
01 Jan 2023
TL;DR: In this article , a deep learning model derived from U-Net was proposed to process all the perfusion parameter maps parallelly at the same time independently, which helps in avoiding the necessity of developing and training different models to process perfusion maps independently.
Abstract: Ischemic stroke is a cerebrovascular disease caused by a blockage in blood vessels of the brain. The early detection of the stroke helps in preventing the penumbra from turning into the core. So, early detection is essential. But the variability of the stroke lesion in size, location, and appearance makes the automatic segmentation of the stroke lesion difficult. Computed Tomography Perfusion (CTP) is more suitable because of its wide availability and the less acquisition time as compared to Magnetic Resonance Imaging (MRI). CTP parameter maps include Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT), and Time to Peak (Tmax). In this paper, we propose a deep learning model derived from U-Net that can process all the perfusion parameter maps parallelly at the same time independently. This architecture helps in avoiding the necessity of developing and training different models to process the perfusion maps independently. The significant modifications in the proposed model are i) incorporation of group convolutions to process the parameter maps separately and ii) introduced element-wise summation of feature maps instead of concatenation. Also, the class imbalance problem in medical datasets makes the segmentations more challenging. This is overcome by employing a loss that is a combination of cross entropy and soft dice loss. The model is trained from scratch. We performed a 5-fold cross-validation on the data. The proposed model achieves the highest 0.441 as the dice coefficient in one fold and the average dice score is 0.421. The experimentation is conducted on Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2018 dataset.
Journal ArticleDOI
TL;DR: In this article , the authors used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes.
Abstract: Introduction Computed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done via setting single perfusion thresholds on two or four CTP parameter maps. We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously. Methods We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images. Results XGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy. Discussion Further development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.
References
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Proceedings ArticleDOI
27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

123,388 citations

Proceedings Article
03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

73,978 citations

Proceedings Article
04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

55,235 citations

Proceedings Article
01 Jan 2015
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

49,914 citations

Journal ArticleDOI
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

42,067 citations