  Scientific discoveries are increasingly driven by analyzing large volumes of
image data. Many new libraries and specialized database management systems
(DBMSs) have emerged to support such tasks. It is unclear, however, how well
these systems support real-world image analysis use cases, and how performant
are the image analytics tasks implemented on top of such systems. In this
paper, we present the first comprehensive evaluation of large-scale image
analysis systems using two real-world scientific image data processing use
cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and
TensorFlow) and find that each of them has shortcomings that complicate
implementation or hurt performance. Such shortcomings lead to new research
opportunities in making large-scale image analysis both efficient and easy to
use.
