  Foraminifera are single-celled marine organisms that construct shells that
remain as fossils in the marine sediments. Classifying and counting these
fossils are important in e.g. paleo-oceanographic and -climatological research.
However, the identification and counting process has been performed manually
since the 1800s and is laborious and time-consuming. In this work, we present a
deep learning-based instance segmentation model for classifying, detecting, and
segmenting microscopic foraminifera. Our model is based on the Mask R-CNN
architecture, using model weight parameters that have learned on the COCO
detection dataset. We use a fine-tuning approach to adapt the parameters on a
novel object detection dataset of more than 7000 microscopic foraminifera and
sediment grains. The model achieves a (COCO-style) average precision of $0.78
\pm 0.00$ on the classification and detection task, and $0.80 \pm 0.00$ on the
segmentation task. When the model is evaluated without challenging sediment
grain images, the average precision for both tasks increases to $0.84 \pm 0.00$
and $0.86 \pm 0.00$, respectively. Prediction results are analyzed both
quantitatively and qualitatively and discussed. Based on our findings we
propose several directions for future work, and conclude that our proposed
model is an important step towards automating the identification and counting
of microscopic foraminifera.
