  Organ segmentation is a fundamental task in medical imaging since it is
useful for many clinical automation pipelines. However, some tasks do not
require full segmentation. Instead, a classifier can identify the selected
organ without segmenting the entire volume. In this study, we demonstrate a
classifier based method to obtain organ labels in real time by using a large
context size with a sparse data sampling strategy. Although our method operates
as an independent classifier at query locations, it can generate full
segmentations by querying grid locations at any resolution, offering faster
performance than segmentation algorithms. We compared our method with existing
segmentation techniques, demonstrating its superior runtime potential for
practical applications in medical imaging.
