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This paper describes the research made toward improving medical case retrieval for Alzheimer's Disease (AD). Our approach considers using Magnetic Resonance Images as an input for the search. To improve the retrieval process, we used longitudinal information extracted from the different sets of scans acquired at different time points and automatically extracted descriptors to represent input images. All experiments were performed with and without quality control (QC) to determine the influence of the errors caused by the automated processing to the results relevance. For the experiments, a total of 267 subjects from the AD Neuroimaging Initiative database with available scans at baseline, the 6-month, 12-month, and 24-month follow-ups were selected. The obtained results showed that the selection of the time points for extraction of the longitudinal information influences the retrieval performance. Results also showed that not all automatically generated descriptors lead to improvement of the results. Longitudinal volume changes provide the most relevant representation. Adding QC phase in the experiments leads to improvements in all examined scenarios. The results showed that the most frequent automatically selected features are common semantic markers for AD.

Longitudinal Brain MRI Retrieval for Alzheimer’s Disease Using Different Temporal Information