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.

翻译：近期，基于深度学习的方法在细胞核检测与分类应用中取得了显著成效。然而，此类方法需依赖大量像素级标注数据进行训练，过程耗时费力，尤其在三维图像场景中更为突出。一种替代方案是采用弱标注方法（例如为每个细胞核标注一个点），但其原始设计针对二维组织病理学图像，无法直接扩展至三维免疫荧光图像。这是因为三维图像包含细胞核与不同标志物在多个通道（z轴）上的独立信息，导致点标注训练难以有效实施。针对这一挑战，我们提出了标签高效对比学习（LECL）模型，用于检测和分类三维免疫荧光图像中的多种类型细胞核。现有方法通常采用最大强度投影（MIP）将多层免疫荧光图像转换为二维图像，但该过程可能使不同z轴层级的信号产生伪关联。为此，我们设计了扩展最大强度投影（EMIP）方法以解决MIP的固有缺陷。此外，针对弱监督场景我们还引入了监督对比学习（SCL）策略。在心血管数据集上的实验表明，所提出的框架能够高效准确地实现三维免疫荧光图像中多种类型细胞核的检测与分类。