Fossil single-celled marine organisms known as foraminifera are widely used in oceanographic research. The identification of species is one of the most common tasks when analyzing ocean samples. One of the primary criteria for species identification is their morphology. Automatic segmentation of images of foraminifera would aid on the identification task as well as on other morphological studies. We pose this problem as an edge detection task for which capturing the correct topological structure is essential. Due to the presence of soft edges and even unclosed segments, state-of-the-art techniques have problems capturing the correct edge structure. Standard pixel-based loss functions are also sensitive to small deformations and shifts of the edges penalizing location more heavily than actual structure. Hence, we propose a homology-based detector of local structural difference between two edge maps with a tolerable deformation. This detector is employed as a new criterion for the training and design of data-driven approaches that focus on enhancing these structural differences. Our approaches demonstrate significant improvement on morphological segmentation of foraminifera when considering region-based and topology-based metrics. Human ranking of the quality of the results by marine researchers also supports these findings.

化石单细胞海洋生物被称为有孔虫，被广泛用于海洋研究。在分析海洋样本时，物种识别是最常见的任务之一。物种识别的主要标准之一是它们的形态。有孔虫图像的自动分割将有助于识别任务以及其他形态学研究。我们将这个问题作为边缘检测任务来解决，对于该任务而言，获取正确的拓扑结构至关重要。由于存在柔软的边缘甚至未封闭的段，因此，现有技术难以捕获正确的边缘结构。基于标准像素的损失函数对边缘的微小变形和移位也很敏感，这会比实际结构更严重地惩罚位置。因此，我们提出了一种基于同源性的具有可容忍变形的两个边缘图之间局部结构差异的检测器。该检测器被用作训练和设计数据驱动方法的新标准，这些方法着重于增强这些结构差异。当考虑基于区域和基于拓扑的指标时，我们的方法证明了有孔虫的形态学分割方面的显着改进。海洋研究人员对结果质量的人类排名也支持了这些发现。当考虑基于区域和基于拓扑的度量时，我们的方法证明了有孔虫的形态学分割方面的显着改进。海洋研究人员对结果质量的人类排名也支持了这些发现。当考虑基于区域和基于拓扑的度量时，我们的方法证明了有孔虫的形态学分割方面的显着改进。海洋研究人员对结果质量的人类排名也支持了这些发现。