Two major causes of death in the United States and worldwide are stroke and
myocardial infarction. The underlying cause of both is thrombi released from
ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the
heart (myocardial infarction) or the brain (stroke). Clinical studies show that
plaque composition plays a more important role than lesion size in plaque
rupture or erosion events. To determine the plaque composition, various cell
types in 3D cardiovascular immunofluorescent images of plaque lesions are
counted. However, counting these cells manually is expensive, time-consuming,
and prone to human error. These challenges of manual counting motivate the need
for an automated approach to localize and count the cells in images. The
purpose of this study is to develop an automatic approach to accurately detect
and count cells in 3D immunofluorescent images with minimal annotation effort.
In this study, we used a weakly supervised learning approach to train the
HoVer-Net segmentation model using point annotations to detect nuclei in
fluorescent images. The advantage of using point annotations is that they
require less effort as opposed to pixel-wise annotation. To train the HoVer-Net
model using point annotations, we adopted a popularly used cluster labeling
approach to transform point annotations into accurate binary masks of cell
nuclei. Traditionally, these approaches have generated binary masks from point
annotations, leaving a region around the object unlabeled (which is typically
ignored during model training). However, these areas may contain important
information that helps determine the boundary between cells. Therefore, we used
the entropy minimization loss function in these areas to encourage the model to
output more confident predictions on the unlabeled areas. Our comparison
studies indicate that the HoVer-Net model trained using our weakly ...

本研究旨在开发一种自动计数血管壁动脉粥样硬化斑块细胞的方法，并使用弱监督学习方法训练 HoVer-Net 模型来检测荧光图像中的细胞核，并使用熵最小化损失函数来提高该模型在周围未标记区域的预测置信度。