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

Maier, Oskar; Wilms, Matthias; Gablentz, Janina von der; Krämer, Ulrike M.; Münte, Thomas F.; Handels, Heinz

doi:10.1016/j.jneumeth.2014.11.011

Cited by 142 publications

(82 citation statements)

References 18 publications

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“…While an increasing number of automatic solutions are presented, there are also a number of semi-automatic methods indicating the difficulty of the task. Among the automatic algorithms, only a few employ pattern classification techniques to learn a segmentation function (Prakash et al, 2006; Maier et al, 2014, 2015c) or design probabilistic generative models of the lesion formation (Derntl et al, 2015; Menze et al, 2015; Forbes et al, 2010; Kabir et al, 2007; Martel et al, 1999). …”

Section: Introductionmentioning

confidence: 99%

“…Where provided, the definition of acute stroke often mixes with the sub-acute phase (Ghosh et al, 2014; Mah et al, 2014; Tsai et al, 2014). Only a few studies give details on pathological inclusion and exclusion criteria of the data (James et al, 2006; Maier et al, 2015c), although these are important characteristics: Results obtained on right-hemispheric stroke only (Dastidar et al, 2000) are not comparable to ones omitting small lesions (Mah et al, 2014) nor to those obtained from two central axial slices of each volume (Li et al, 2004). Comparability is further impeded by a wide range of dataset sizes ( N ∈ [2, 57]), employed MRI sequences and quantitative evaluation measures.…”

Section: Introductionmentioning

confidence: 99%

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ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Maier

Menze

Gablentz

et al. 2017

Medical Image Analysis

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435

322

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Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Maier

Menze

Gablentz

et al. 2017

Medical Image Analysis

Self Cite

435

322

View full text Add to dashboard Cite

show abstract

“…To evaluate our method we used the SISS brain multimodal MR dataset from the ISLES 2015 workshop [17] and we compared with a classical patch-based approach [29]. Overall, our findings show that the proposed method is capable of preserving anatomical details.…”

Section: Introductionmentioning

confidence: 92%

“…We used the SISS dataset from the ISLES 2015 workshop [17]. Specifically, we used the training dataset, which includes 28 subjects.…”

Section: Datasetmentioning

confidence: 99%

Whole Image Synthesis Using a Deep Encoder-Decoder Network

Sevetlidis

Giuffrida

Tsaftaris

2016

Simulation and Synthesis in Medical Imaging

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The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods follow a patchbased approach, which is computationally inefficient during synthesis and requires some sort of 'fusion' to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resembles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn't require extensive amounts of memory, and produces comparable results to recent patch-based approaches.

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“…Ischemic brain stroke is one of the leading causes of death and disability in major industrialized countries [5]. To detect the Ischemic brain cells, the computed tomography (CT) and Magnetic Resonance Image (MRI) is the two important screening tests [6,7]. The existing methods used the conventional threshold techniques, but this method only has the limited accuracy and repeatability [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

Melingi¹,

Vivekanand²

2018

IJIES

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An Ischemic stroke is expressed as lost neurological brain work because of the sudden loss of blood dissemination in the specific territory of the brain. The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. In this paper we utilize a hybrid way to deal with detecting the ischemic stroke from the alternate pathologies in magnetic resonance (MR) images utilizing Kernelized Fuzzy C-means (KFCM) clustering with adaptive threshold algorithm and the Support Vector Machine (SVM) classifier. In the existing method, the Otsu's method incorporated with SVM classifier method is utilized for the segmentation of the ischemic stroke image, but it has the limited accuracy 88%, specificity 66% and the sensitivity value is 94%. For the exact identification and segmentation the KFCM algorithm is utilized. The distance and the intensity of the lesion tissue is identified by this method. The accuracy and segmentation aftereffects of the classifier are measured in the testing and training phase by looking at the comparable and a decent variety of sample sets by considering diverse groupings. Our test comes about demonstrating that, the performance of the proposed technique is assessed in view of the precision, recall, sensitivity, accuracy and overlap metrics of the framework. Compared with the existing classification method, the proposed method has 17.64% RMSE (Root Mean Square Error), 6.24% MAPE (Mean Absolute Percentage Error) and 2.55% MBE (Mean Bias Error), consumption time is 6.45 (s) and also the sensitivity and the accuracy ranges are 98.8% and 99%. The proposed approach is actualized using MATLAB and the realtime datasets are used for our examination.

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

Cited by 142 publications

References 18 publications

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Whole Image Synthesis Using a Deep Encoder-Decoder Network

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

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