Abstract. Vertebrate microfossils have broad applications in evolutionary
biology and stratigraphy research areas such as the evolution of hard
tissues and stratigraphic correlation. Classification is one of the basic
tasks of vertebrate microfossil studies. With the development of techniques
for virtual paleontology, vertebrate microfossils can be classified
efficiently based on 3D volumes. The semantic segmentation of different
fossils and their classes from CT data is a crucial step in the
reconstruction of their 3D volumes. Traditional segmentation methods adopt
thresholding combined with manual labeling, which is a time-consuming process. Our
study proposes a deep-learning-based (DL-based) semantic segmentation method for
vertebrate microfossils from CT data. To assess the performance of the
method, we conducted extensive experiments on nearly 500 fish microfossils.
The results show that the intersection over union (IoU) performance metric
arrived at least 94.39 %, meeting the semantic segmentation requirements
of paleontologists. We expect that the DL-based method could also be applied
to other fossils from CT data with good performance.