  Considerable morphological phenotyping studies in nephrology have emerged in
the past few years, aiming to discover hidden regularities between clinical and
imaging phenotypes. Such studies have been largely enabled by deep learning
based image analysis to extract sparsely located targeting objects (e.g.,
glomeruli) on high-resolution whole slide images (WSI). However, such methods
need to be trained using labor-intensive high-quality annotations, ideally
labeled by pathologists. Inspired by the recent "human-in-the-loop" strategy,
we developed EasierPath, an open-source tool to integrate human physicians and
deep learning algorithms for efficient large-scale pathological image
quantification as a loop. Using EasierPath, physicians are able to (1) optimize
the recall and precision of deep learning object detection outcomes adaptively,
(2) seamlessly support deep learning outcomes refining using either our
EasierPath or prevalent ImageScope software without changing physician's user
habit, and (3) manage and phenotype each object with user-defined classes. As a
user case of EasierPath, we present the procedure of curating large-scale
glomeruli in an efficient human-in-the-loop fashion (with two loops). From the
experiments, the EasierPath saved 57 % of the annotation efforts to curate
8,833 glomeruli during the second loop. Meanwhile, the average precision of
glomerular detection was leveraged from 0.504 to 0.620. The EasierPath software
has been released as open-source to enable the large-scale glomerular
prototyping. The code can be found in https://github.com/yuankaihuo/EasierPath
