  Lack of enough labeled data is a major problem in building machine learning
based models when the manual annotation (labeling) is error-prone, expensive,
tedious, and time-consuming. In this paper, we introduce an iterative deep
learning based method to improve segmentation and counting of cells based on
unbiased stereology applied to regions of interest of extended depth of field
(EDF) images. This method uses an existing machine learning algorithm called
the adaptive segmentation algorithm (ASA) to generate masks (verified by a
user) for EDF images to train deep learning models. Then an iterative deep
learning approach is used to feed newly predicted and accepted deep learning
masks/images (verified by a user) to the training set of the deep learning
model. The error rate in unbiased stereology count of cells on an unseen test
set reduced from about 3 % to less than 1 % after 5 iterations of the iterative
deep learning based unbiased stereology process.
