  Recently, deep learning-based methods achieved promising performance in
nuclei detection and classification applications. However, training deep
learning-based methods requires a large amount of pixel-wise annotated data,
which is time-consuming and labor-intensive, especially in 3D images. An
alternative approach is to adapt weak-annotation methods, such as labeling each
nucleus with a point, but this method does not extend from 2D histopathology
images (for which it was originally developed) to 3D immunofluorescent images.
The reason is that 3D images contain multiple channels (z-axis) for nuclei and
different markers separately, which makes training using point annotations
difficult. To address this challenge, we propose the Label-efficient
Contrastive learning-based (LECL) model to detect and classify various types of
nuclei in 3D immunofluorescent images. Previous methods use Maximum Intensity
Projection (MIP) to convert immunofluorescent images with multiple slices to 2D
images, which can cause signals from different z-stacks to falsely appear
associated with each other. To overcome this, we devised an Extended Maximum
Intensity Projection (EMIP) approach that addresses issues using MIP.
Furthermore, we performed a Supervised Contrastive Learning (SCL) approach for
weakly supervised settings. We conducted experiments on cardiovascular datasets
and found that our proposed framework is effective and efficient in detecting
and classifying various types of nuclei in 3D immunofluorescent images.
