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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14

Name: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14
Keywords: Brain Segmentation, Pixel Attribution, Segmentation Saliency Maps, Network Interpretability, Image Augmentation

Research datakeyboard_double_arrow_right Dataset 28 Oct 2022 English Publisher:Zenodo

Authors: Suhang You; Mauricio Reyes;

doi: 10.5281/zenodo.7393173 , 10.5281/zenodo.7393174

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14

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Abstract

This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the seventh part of 14 parts of the full dataset (7/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 40ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622

Related Organizations

University of Bern
Switzerland

Keywords

Brain Segmentation, Pixel Attribution, Segmentation Saliency Maps, Network Interpretability, Image Augmentation

13 Research products, page 1 of 2

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