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：近年来，基于深度学习的方法在细胞核检测与分类应用中取得了良好性能。然而，训练此类方法需要大量像素级标注数据，这一过程耗时且费力，尤其在三维图像中。替代方案是采用弱标注方法（如为每个细胞核标注一个点），但该方法无法从最初开发的二维组织病理图像扩展到三维免疫荧光图像。原因是三维图像包含多个通道（z轴）分别对应细胞核和不同标记物，这使得基于点标注的训练变得困难。为解决这一挑战，我们提出标签高效对比学习（LECL）模型，用于在三维免疫荧光图像中检测和分类多种类型的细胞核。先前方法采用最大强度投影（MIP）将多切片免疫荧光图像转换为二维图像，这可能导致不同z层信号被错误关联。为克服此问题，我们设计了扩展最大强度投影（EMIP）方法来解决MIP带来的问题。此外，我们针对弱监督场景实施了监督对比学习（SCL）方法。通过在心血管数据集上的实验，我们发现所提出的框架在检测和分类三维免疫荧光图像中多种类型细胞核时既有效又高效。