ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).

[1]  Mauricio Reyes,et al.  On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation , 2018, MICCAI.

[2]  Victor Alves,et al.  Enhancing interpretability of automatically extracted machine learning features: application to a RBM‐Random Forest system on brain lesion segmentation , 2018, Medical Image Anal..

[3]  D. Rueckert,et al.  White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.

[4]  Verónica Vilaplana,et al.  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2017, Lecture Notes in Computer Science.

[5]  Konstantinos Kamnitsas,et al.  Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.

[6]  S. Bauer,et al.  Fully automated stroke tissue estimation using random forest classifiers (FASTER) , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[7]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

[8]  Kilian Q. Weinberger,et al.  On Calibration of Modern Neural Networks , 2017, ICML.

[9]  Liang Chen,et al.  Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks , 2017, NeuroImage: Clinical.

[10]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[11]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[14]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[15]  et al.,et al.  ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI , 2017, Medical Image Anal..

[16]  Spyridon Bakas,et al.  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2017, Lecture Notes in Computer Science.

[17]  Christian Lucas,et al.  Shallow Fully-Connected Neural Networks for Ischemic Stroke-Lesion Segmentation in MRI , 2017, Bildverarbeitung für die Medizin.

[18]  Jaime S. Cardoso,et al.  Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , 2017, Lecture Notes in Computer Science.

[19]  Andreas Holzinger,et al.  Machine Learning for Health Informatics , 2016, Lecture Notes in Computer Science.

[20]  Camille Couprie,et al.  Semantic Segmentation using Adversarial Networks , 2016, NIPS 2016.

[21]  Joong-Ho Won,et al.  Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke , 2016, BrainLes@MICCAI.

[22]  Mohammad Havaei,et al.  Deep Learning Trends for Focal Brain Pathology Segmentation in MRI , 2016, Machine Learning for Health Informatics.

[23]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[24]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[25]  Eugenio Culurciello,et al.  ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation , 2016, ArXiv.

[26]  Stephen J. McKenna,et al.  Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

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

[28]  Gábor Székely,et al.  A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation— With Application to Tumor and Stroke , 2016, IEEE Transactions on Medical Imaging.

[29]  Hao Chen,et al.  Deep Contextual Networks for Neuronal Structure Segmentation , 2016, AAAI.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Hao Chen,et al.  Automatic detection of cerebral microbleeds via deep learning based 3D feature representation , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[32]  H. Handels,et al.  Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences , 2015, Journal of Neuroscience Methods.

[33]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[35]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[36]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[37]  J. Gee,et al.  The Insight ToolKit image registration framework , 2014, Front. Neuroinform..

[38]  Michael Kistler,et al.  The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration , 2013, Journal of medical Internet research.

[39]  Assoc. Prof. Leon Abdillah.  Data Structures & Algorithms , 2013 .

[40]  Geoffrey E. Hinton,et al.  Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition-' Washington , D . C . , June , 1983 OPTIMAL PERCEPTUAL INFERENCE , 2011 .

[41]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[42]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[43]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[44]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[45]  IEEE conference on computer vision and pattern recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).