Deep learning-based segmentation and classification are crucial to large-scale biomedical imaging, particularly for 3D data, where manual analysis is impractical. Although many methods exist, selecting suitable models and tuning parameters remains a major bottleneck in practice. Hence, we introduce the 3D data Analysis Optimization Pipeline, a method designed to facilitate the design and parameterization of segmentation and classification using two Bayesian Optimization stages. First, the pipeline selects a segmentation model and optimizes postprocessing parameters using a domain-adapted syntactic benchmark dataset. To ensure a concise evaluation of segmentation performance, we introduce a segmentation quality metric that serves as the objective function. Second, the pipeline optimizes design choices of a classifier, such as encoder and classifier head architectures, incorporation of prior knowledge, and pretraining strategies. To reduce manual annotation effort, this stage includes an assisted class-annotation workflow that extracts predicted instances from the segmentation results and sequentially presents them to the operator, eliminating the need for manual tracking. In four case studies, the 3D data Analysis Optimization Pipeline efficiently identifies effective model and parameter configurations for individual datasets.

翻译：基于深度学习的分割与分类在大规模生物医学成像中至关重要，尤其对于手动分析不切实际的三维数据。尽管已有多种方法，但在实践中选择合适的模型并调整参数仍是主要瓶颈。为此，我们提出了三维数据分析优化流程，该方法通过两个贝叶斯优化阶段来促进分割与分类的设计与参数化。首先，该流程利用领域适应的合成基准数据集选择分割模型并优化后处理参数。为确保对分割性能的简洁评估，我们引入了作为目标函数的分割质量度量指标。其次，该流程优化分类器的设计选择，例如编码器与分类头架构、先验知识的整合以及预训练策略。为减少人工标注工作量，此阶段包含辅助类别标注工作流，该流程从分割结果中提取预测实例并顺序呈现给操作者，从而无需手动追踪。在四个案例研究中，三维数据分析优化流程高效地为各数据集识别出有效的模型与参数配置。