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.

翻译：深度学习与医学图像分割领域中最具挑战性的问题之一是如何为每个像素的分类确定合适的阈值。该阈值是一个临界值，当模型输出超过此值时，该像素被认为属于特定类别。基于个人经验的手动阈值分割不仅容易出错且耗时较长，尤其在处理医学图像这类复杂问题时更为突出。传统阈值分割方法难以有效确定此类问题所需的阈值。为应对这一挑战，研究者提出了基于深度学习的自动阈值分割方法。然而，这类方法的核心问题在于其通常静态地确定阈值，未考虑输入数据的变化。由于输入数据可能具有动态性并随时间变化，阈值的确定应具备自适应能力，能够根据输入数据和环境条件进行动态调整。