# Iterative Deep Learning Based Unbiased Stereology With Human-in-the-Loop

## TL;DR

This paper presents an iterative deep learning approach with human-in-the-loop to improve cell segmentation and counting in stereology, significantly reducing error rates in EDF images.

## Contribution

It introduces a novel iterative deep learning method combined with human verification to enhance unbiased stereology in cell counting tasks.

## Key findings

- Error rate reduced from 3% to less than 1% after 5 iterations
- Uses adaptive segmentation algorithm to generate training masks
- Iterative process improves model accuracy with human-in-the-loop verification

## Abstract

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.