An appraisal of the performance of AI tools for chronic stroke lesion segmentation

[1]  Buket Kaya,et al.  A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images , 2023, Int. J. Imaging Syst. Technol..

[2]  G. Zaharchuk,et al.  Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network , 2022, NeuroImage: Clinical.

[3]  Donghyeon Kim,et al.  Fine-grained brain tissue segmentation for brain modeling of stroke patient , 2022, Comput. Biol. Medicine.

[4]  B. Raza,et al.  The multi-level classification network (MCN) with modified residual U-Net for ischemic stroke lesions segmentation from ATLAS , 2022, Comput. Biol. Medicine.

[5]  Zelin Wu,et al.  Multi-scale long-range interactive and regional attention network for stroke lesion segmentation , 2022, Comput. Electr. Eng..

[6]  Soumya Snigdha Kundu,et al.  A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images , 2022, Comput. Methods Programs Biomed..

[7]  D. Paydarfar,et al.  Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images , 2022, Diagnostics.

[8]  Youyi Song,et al.  Mutual gain adaptive network for segmenting brain stroke lesions , 2022, Appl. Soft Comput..

[9]  Xiaodong Zhang,et al.  Multi-Task Learning Improves the Brain Stoke Lesion Segmentation , 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Guorong Cai,et al.  MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation , 2022, Concurr. Comput. Pract. Exp..

[11]  Chih-Chung Hsu,et al.  Semantic segmentation guided detector for segmentation, classification, and lesion mapping of acute ischemic stroke in MRI images , 2022, NeuroImage: Clinical.

[12]  Zhongjie Chen,et al.  Cross-Attention and Deep Supervision UNet for Lesion Segmentation of Chronic Stroke , 2022, Frontiers in Neuroscience.

[13]  Jing Wang,et al.  METrans: Multi‐encoder transformer for ischemic stroke segmentation , 2022, Electronics Letters.

[14]  Xiuquan Du,et al.  AGMR-Net: Attention-guided multiscale recovery framework for stroke segmentation , 2022, Comput. Medical Imaging Graph..

[15]  M. Chang,et al.  Automated segmentation of chronic stroke lesion using efficient U-Net architecture , 2022, Biocybernetics and Biomedical Engineering.

[16]  A. Gramfort,et al.  A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms , 2021, Scientific Data.

[17]  M. Seghier Ten simple rules for reporting machine learning methods implementation and evaluation on biomedical data , 2021, Int. J. Imaging Syst. Technol..

[18]  Wenming Yang,et al.  MDAN: Mirror Difference Aware Network for Brain Stroke Lesion Segmentation , 2021, IEEE Journal of Biomedical and Health Informatics.

[19]  Stefano Soatto,et al.  Small Lesion Segmentation in Brain MRIs with Subpixel Embedding , 2021, BrainLes@MICCAI.

[20]  Xueying Zhang,et al.  Dual-Path Attention Compensation U-Net for Stroke Lesion Segmentation , 2021, Comput. Intell. Neurosci..

[21]  Ben Glocker,et al.  Normative ascent with local gaussians for unsupervised lesion detection , 2021, Medical Image Anal..

[22]  Hritam Basak,et al.  DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation , 2021, SN Computer Science.

[23]  Yuqi Fang,et al.  Hierarchically Spatial Encoding Module for Chronic Stroke Lesion Segmentation , 2021, 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER).

[24]  Supan Tungjitkusolmun,et al.  Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning , 2021, Sensors.

[25]  Konstantinos Kamnitsas,et al.  Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation , 2020, IEEE Transactions on Medical Imaging.

[26]  U. Jayaraman,et al.  A Novel Modified U-shaped 3-D Capsule Network (MUDCap3) for Stroke Lesion Segmentation from Brain MRI , 2020, 2020 IEEE 4th Conference on Information & Communication Technology (CICT).

[27]  Yifan Chen,et al.  A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation , 2020, MLMI@MICCAI.

[28]  Rahul C. Deo,et al.  Recommendations for Reporting Machine Learning Analyses in Clinical Research , 2020, Circulation. Cardiovascular quality and outcomes.

[29]  Baiying Lei,et al.  Brain stroke lesion segmentation using consistent perception generative adversarial network , 2020, Neural Computing and Applications.

[30]  Cuntai Guan,et al.  Minimizing Hybrid Dice Loss for Highly Imbalanced 3D Neuroimage Segmentation , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[31]  Yue Zhang,et al.  MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images , 2020, IEEE Journal of Biomedical and Health Informatics.

[32]  Lipo Wang,et al.  3D Deep Learning on Medical Images: A Review , 2020, Sensors.

[33]  Philip Smith,et al.  Reporting quality of studies using machine learning models for medical diagnosis: a systematic review , 2020, BMJ Open.

[34]  Lee A. Baugh,et al.  The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke , 2020, Human brain mapping.

[35]  Wangmeng Zuo,et al.  Deep Learning on Image Denoising: An overview , 2019, Neural Networks.

[36]  Kehan Qi,et al.  MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation , 2019, IEEE Access.

[37]  Saeed Hassanpour,et al.  Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network , 2019, NeuroImage: Clinical.

[38]  Yong Xia,et al.  D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[39]  Qiegen Liu,et al.  X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies , 2019, MICCAI.

[40]  Hairong Zheng,et al.  CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke , 2019, MICCAI.

[41]  Jeffrey R. Binder,et al.  A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images , 2019, NeuroImage: Clinical.

[42]  H. H. Thodberg,et al.  The RSNA Pediatric Bone Age Machine Learning Challenge. , 2019, Radiology.

[43]  Ender Konukoglu,et al.  Unsupervised Lesion Detection via Image Restoration with a Normative Prior , 2018, MIDL.

[44]  Yuko Nakamura,et al.  Improvement of image quality at CT and MRI using deep learning , 2018, Japanese Journal of Radiology.

[45]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[46]  Wei Luo,et al.  Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View , 2016, Journal of medical Internet research.

[47]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[48]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[51]  M. Drummond,et al.  Improving medical device regulation: the United States and Europe in perspective. , 2014, The Milbank quarterly.

[52]  D. Moher,et al.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement , 2009, BMJ : British Medical Journal.

[53]  Anil F. Ramlackhansingh,et al.  Lesion identification using unified segmentation-normalisation models and fuzzy clustering , 2008, NeuroImage.

[54]  Lorraine K. Tyler,et al.  Identifying lesions on structural brain images—Validation of the method and application to neuropsychological patients , 2005, Brain and Language.

[55]  Chris Rorden,et al.  Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking , 2001, NeuroImage.

[56]  Frithjof Kruggel,et al.  Segmentation of large brain lesions , 2001, IEEE Transactions on Medical Imaging.

[57]  J. Dias,et al.  SIPFormer: Segmentation of Multiocular Biometric Traits With Transformers , 2023, IEEE Transactions on Instrumentation and Measurement.

[58]  Guorong Cai,et al.  A Multiple Encoders Network for Stroke Lesion Segmentation , 2021, PRCV.

[59]  Xueying Zhang,et al.  A Partitioning-Stacking Prediction Fusion Network Based on an Improved Attention U-Net for Stroke Lesion Segmentation , 2020, IEEE Access.

[60]  S. B. Martins Unsupervised brain anomaly detection in MR images , 2020 .

[61]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[62]  Lei Ai,et al.  A large, open source dataset of stroke anatomical brain images and manual lesion segmentations , 2017, bioRxiv.