Statistics and Data Science in Imaging

Spatial Statistics in Imaging

Under the support of the American Statistical Association (ASA) sections on Statistics and the Environment (ENVR) and Statistics in Epidemiology (SIE), we invite submissions to a special issue of “Spatial Statistics in Imaging.” This special Issue aims to highlight recent advances in statistical methodology and computation in spatial statistics and spatial modeling of image data that arise in agriculture, climate science, remote sensing, neuroimaging, genomics and other scientific fields requiring advanced spatial statistical methods. We welcome contributions that address methodological innovations for spatial, spatio-temporal, and high-dimensional image data. Papers integrating modern statistical methods with machine learning, deep learning, or functional data analysis for imaging applications are encouraged, as are contributions to scalable computational approaches, uncertainty quantification, and multi-resolution or multi-modal data integration. Both methodological developments and application-driven case studies are welcome, particularly those demonstrating the impact of spatial statistical thinking in extracting insight from complex imaging data.

In addition to the types of articles published by the journal, as described below, short papers on high-quality research can also be submitted for consideration in this special Issue. Manuscripts submitted will need to meet the same requirements and standards as regular submissions. The SDSI Editor-in-Chief will conduct initial screenings of all submissions and make assignments to the guest Editors, who will oversee the peer-review process. To ensure efficiency in the review and decision-making process while maintaining a high standard of academic rigor, we aim for a review time not exceeding 10 weeks from submission to first decision.

Statistics and Data Science in Imaging is published on behalf of the ASA, under the support of its Section on Statistics in Imaging. It is an international open access journal and only publishes open access articles. Publishing open access means that your article will be free to access online immediately on publication, increasing the visibility, readership, and impact of your research.

There are currently no APCs (article publishing charges) to publish in SDSI.

The journal publishes original research and reviews on Statistical Analysis of Imaging Data. The primary aim of the journal is to serve as a forum for discussing methodological challenges encountered in the analysis of imaging data and for presenting statistically sound solutions to those challenges. The journal covers a broad spectrum of statistical methods and data science techniques applicable to various imaging domains, including, but not limited to, neuroimaging, medical imaging, satellite imaging, physics, forensic imaging, astronomy, remote sensing, and materials science. The target audience comprises quantitative researchers, including statisticians, engineers, computer scientists, and data scientists, along with imaging researchers in fields such as brain science, radiology, satellite, forensic imaging, environmental studies, who are involved in developing and investigating methods for analyzing imaging data.

Along with methodological research papers, the journal publishes discussion papers, soliciting concise feedback from the statistical imaging community, in-depth reviews of specific topics by leading statisticians and data scientists, case-study papers that highlight applications of statistical methods in imaging data analysis to address real world questions, short communications highlight emerging issues of interest to the statistical community, and best practices papers for pipelines of data pre-processing and analysis to facilitate access to public data repositories and use of statistical software.

For further inquiries about the SDSI Special Issue, contact the SDSI Editor-in-Chief or any of the guest Editors.