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

Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images