Supervised ML without manual annotation? Makes sense for 3D data. But how? See our 3D UNet implementation for small animal brain masking at today's #emim2021 poster session w/ @hendrik_klug
PDF | In biomedical imaging, between-subject comparability is attained at the voxel level via image registration. This process requires several data... | Find, read and cite all the research you need on ResearchGate
Whole-brain map of VTA dopaminergic stilumation in the mouse, as seen at #ISMRM2020
Analysis and data fully open source and accessible for hacking/reproduction: https://bitbucket.org/TheChymera/opfvta/src
#freeandopenscience #opendata #python #linux #neuroscience #SAMRI
Brief 4' command-line demo for automatic Bruker Paravision to BIDS repositing.
#neuroscience #freeandopenscience #FOSS #Python #linux #bash #datascience #neuroinformatics #preclinical #fMRI #mouseMRI #neuroimaging #samri #bru2bids
The newest revision of our small animal brain registration pipeline and benchmarking article, as accepted for review <3
https://www.biorxiv.org/content/10.1101/619650v2
#freeandopenscience #freeanimalresearch #neuroscience #fMRI #preprocessing #smallanmimalMRI #FOSS #SAMRI #ANTs #Python #RepSeP
The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We present four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 3-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, important criteria for the comparability of scientific results across experiments and centers.
Our newest manuscript --- improving transparency, inter-study comparability, and robustness of preclinical MRI registration --- is out. Get it while it's hot!
#freeandopenscience #nipype #samri #fmri #datascience #freeanimalresearch #neuroscience
Given the need for comparability across subjects and studies, the quality of registration to a standard space is crucial for the reliability of Magnetic Resonance Imaging (MRI), and in particular functional MRI (fMRI). Present small animal MRI workflows fall short in terms of quality and reliability, commonly utilizing high-level scripts optimized for human data (adapting data to the scripts rather than vice-versa), and relying on interactive operator quality control (QC), which is infrequent, open to bias, slow, and unreproducible. In this fully reproducible article we showcase a novel mouse-brain-optimized workflow (accessible via Bash and Python), and a standard space suited to harmonize data between analysis and operation. We present four separate metrics for automated QC, and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices (which we detail and comment) reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves volume conservation RMSE 2.8-fold, and smoothness conservationRMSE 2.9-fold. The "SAMRI Generic" workflow setsa new standard for small animal MRI registration, en-suring robustness, comparability, and validity of region assignment.
fMRI preprocessing and data analysis intro, using animal data and cutting-edge software, including #RepSeP, #SAMRI, #nilearn, and #nipype.
#ethzurich #zurich #freeandopenscience #sciencecommunication #freeanimalresearch #openeducation
#ImageOfThemonth at the Animal Imaging Center: ROI histograms for my favorite stimulation protocol.
Analysis and plots via: https://github.com/IBT-FMI/SAMRI
#AnimalImaging #fMRI #serotonin #DataViz #neuroscience #SAMRI
2018-01 Picture of the Month at the Animal Imaging Center:
Visual Inspection of multi-session registration coherence - with #SAMRI (https://github.com/IBT-FMI/SAMRI).
#openscience #freeanimalresearch https://social.tchncs.de/media/rRd5oQ6DHUzY8ylrAB8
Horea Christian
Deutsche Welle (inoffiziell)