An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation.

翻译：当代生物医学领域一个悬而未决的问题是大量复杂且多样化的图像需要进行标注、分析与解读。虽然深度学习的最新进展已颠覆计算机视觉领域，催生了在图像分割任务中能与人类专家匹敌的算法，但这些框架需要大量人工标注数据集进行训练，且生成的模型难以解释。本研究提出Kartezio——一种基于模块化笛卡尔遗传编程的计算策略，通过迭代组装与参数化计算机视觉函数，生成透明且易于解释的图像处理流水线。该流水线在实例分割任务中展现出与前沿深度学习方法相当的精度，同时所需训练数据集规模显著缩小，这一特性为此方法带来了极大的灵活性、速度与功能性。我们还在四个真实场景中部署Kartezio解决语义分割与实例分割问题，展示了其在从高分辨率显微镜到临床病理学等成像环境中的实用价值。通过成功将Kartezio应用于从亚细胞结构到肿瘤组织等不同尺度的图像组合，我们证明了这一完全可解释的进化设计器在语义分割与实例分割中的灵活性、鲁棒性与实际效用。