# Instance Segmentation of Microscopic Foraminifera
###### Abstract  
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 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 on the classification and detection task, and 0.80 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84 and 0.86, 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. Keywords:foraminifera; instance segmentation; object detection; deep learning +
Footnote †: journal: Medical  
stress to the marine environment [8] and interpreting the ages and paleoenvironments of sedimentary strata in oil wells via bioststratigraphyy [9]. After a sediment core has been retrieved from the seabed, a range of procedures are performed in the laboratory before the foraminiferal specimens can be identified and extracted under the microscope by a geoscientist using a brush or needle. From each core, several layers are extracted, and each layer is regarded as a sample. To establish a statistically robust representation of the fauna, 300-500 specimens are identified and extracted per sample. The time-consumption of this task is 2-8 h/sample, depending on the complexity of the sample and the experience level of the geoscientist. A typical study consists of 100-200 samples from one or several cores, and the overall time-consumption in just identifying the specimens is vast. Recently developed deep learning models show promising results towards automating parts of the identification and extraction process [10; 11; 12; 13; 14]. Figure 1 shows an example of a prepared foraminifera sample--microscopic objects spread out on a plate and photographed through a microscope. Of particular interest is the classification of each object into high-level foraminifera classes, which then serves as input for estimation of the environment in which sediment was produced. This task consists of identifying relevant objects, particularly separate sediment from foraminifera, and recognize foraminifera classes based on shapes and structures of each object. Object classification in images is one of the great successes of deep learning, and conquer new applications as new methods are developed and high-quality data are made available for training and testing. In a deep learning context, a core task of object classification is instance segmentation. Not only must the objects be separated from the background, but the objects themselves must be separated from each other, so that adjacent objects are identified, and not treated as one single object. Automatic foraminifera identification has great practical potential for three main reasons. Firstly, the time saved for highly qualified personnel is substantial, which can then be made available for other tasks. Secondly, it is the overall proportion of foraminifera classes that is the primary interest, and it is, therefore, robust to the occasional misclassification, as opposed to, e.g., cancer detection, where overlooking a small region of cancer cells can be fatal.