Fusulinid foraminifera are among the most common microfossils of the Late Palaeozoic and act as key fossils for stratigraphic correlation, paleogeographic and paleoenvironmental indication, and evolutionary studies of marine life. Accurate and efficient identification forms the basis of such research involving fusulinids but is limited by the lack of digitized image datasets. This article presents the first large image dataset of fusulinids containing 2,400 images of individual samples subjected to 16 genera of all six fusulinid families and labelled to species level. These images were collected from the literature and our unpublished samples through an automatic segmentation procedure implementing BlendMask, a deep learning model. The dataset shows promise for the efficient accumulation of fossil images through automated procedures and will facilitate taxonomists in future morphologic and systematic studies.

Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two state-of-the-art methods.