| Autor: |
Tustison NJ; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA. ntustison@virginia.edu.; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA. ntustison@virginia.edu., Cook PA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Holbrook AJ; Department of Biostatistics, University of California, Los Angeles, CA, USA., Johnson HJ; Department of Electrical and Computer Engineering, University of Iowa, Philadelphia, PA, USA., Muschelli J; School of Public Health, Johns Hopkins University, Baltimore, MD, USA., Devenyi GA; Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada., Duda JT; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Das SR; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Cullen NC; Department of Clinical Sciences, Lund University, Lund, Scania, Sweden., Gillen DL; Department of Statistics, University of California, Irvine, CA, USA., Yassa MA; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA., Stone JR; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA., Gee JC; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Avants BB; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA. |
| Abstrakt: |
The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis. |
