  One of the most significant challenges in the field of deep learning and
medical image segmentation is to determine an appropriate threshold for
classifying each pixel. This threshold is a value above which the model's
output is considered to belong to a specific class. Manual thresholding based
on personal experience is error-prone and time-consuming, particularly for
complex problems such as medical images. Traditional methods for thresholding
are not effective for determining the threshold value for such problems.
  To tackle this challenge, automatic thresholding methods using deep learning
have been proposed. However, the main issue with these methods is that they
often determine the threshold value statically without considering changes in
input data. Since input data can be dynamic and may change over time, threshold
determination should be adaptive and consider input data and environmental
conditions.
