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

Maier, Oskar; Menze, Bjoern; Gablentz, Janina von der; Häni, Levin; Heinrich, Mattias P.; Liebrand, Matthias; Winzeck, Stefan; Basit, Abdul; Bentley, Paul; Chen, Liang; Christiaens, Daan; Dutil, Francis; Egger, Karl; Feng, Chaolu; Glocker, Ben; Goetz, Michael; Haeck, Tom; Halme, Hanna-Leena; Havaei, Mohammad; Iftekharuddin, Khan M.; Jodoin, Pierre Marc; Kamnitsas, Konstantinos; Kellner, Elias; Korvenoja, Antti; Larochelle, Hugo; Ledig, Christian; Lee, Jia Hong; Maes, Frederik; Mahmood, Qaiser; Maier‐Hein, Klaus H.; McKinley, Richard; Muschelli, John; Pal, Chris; Pei, Linmin; Rangarajan, Janaki Raman; Reza, Syed M. S.; Robben, David; Rueckert, Daniel; Salli, Eero; Suetens, Paul; Wang, Ching-Wei; Wilms, Matthias; Kirschke, Jan S.; Krämer, Ulrike M.; Münte, Thomas F.; Schramm, Peter; Wiest, Roland; Handels, Heinz; Reyes, Mauricio

doi:10.1016/j.media.2016.07.009

Cited by 430 publications

(328 citation statements)

References 72 publications

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“…Roy et al (2014b) demonstrated a patch-based lesion segmentation that used examples from an atlas to match patches in the input images using a sparse dictionary approach. Variants of this supervised machine learning solution include: generic machine learning (Xie and Tao, 2011); dictionary learning and sparse-coding (Roy et al, 2014a, 2015b; Weiss et al, 2013); and random forest (RF) work by Mitra et al (2014), variations of the RF approach include Geremia et al (2010, 2011) using multi-channel MR intensities, long-range spatial context, and asymmetry features to identify lesions; Jog et al (2015) producing overlapping lesion masks from the RF that were averaged to create a probabilistic segmentation, and Maier et al (2015) used extra tree forests (Geurts et al, 2006) which are robust to noise and uncertain training data.…”

Section: Introductionmentioning

confidence: 99%

“…These public databases have served to standardize comparisons and evaluation criteria. In recent years there has been a shift in the community to launch these data sets as a challenge associated with a workshop or conference (Styner et al, 2008; Schaap et al, 2009; Heimann et al, 2009; Menze et al, 2015; Mendrik et al, 2015; Maier et al, 2017). In particular, the 2008 MICCAI MS Lesion challenge (Styner et al, 2008) was a significant step forward in the sharing of clinically relevant data.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

et al. 2017

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In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: 1) the sharing of a rich data set; 2) collaboration and comparison of the various avenues of research being pursued in the community; and 3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 1 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

et al. 2017

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“…For stroke, there is currently no consensus for the best modeling tool. However, challenges comparing different approaches, such as the Ischemic Stroke Lesion Segmentation (ISLES) Challenge [28], could be used for a validation of the entropy-based framework proposed here.…”

Section: Discussionmentioning

confidence: 99%

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

Giacalone

Frindel

Grenier

et al. 2017

Entropy

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Abstract:In longitudinal medical studies, multicomponent images of the tissues, acquired at a given stage of a disease, are used to provide information on the fate of the tissues. We propose a quantification of the predictive value of multicomponent images using information theory. To this end, we revisit the predictive information introduced for monodimensional time series and extend it to multicomponent images. The interest of this theoretical approach is illustrated on multicomponent magnetic resonance images acquired on stroke patients at acute and late stages, for which we propose an original and realistic model of noise together with a spatial encoding for the images. We address therefrom very practical questions such as the impact of noise on the predictability, the optimal choice of an observation scale and the predictability gain brought by the addition of imaging components.

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“…Maier et al [21] have developed a common evaluation benchmark framework. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy.…”

Section: Related Workmentioning

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

Cited by 430 publications

References 72 publications

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

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