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.

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology.Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields.The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

Objective: To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods: Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results: The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions: EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development.

Foraminifera are microscopic single-celled organisms, ubiquitous to the marine realm, that construct shells during their life cycle. The shells, in general, fossilize well in the sediment and they are diagnosable due to inter-species morphology and ornamentation variability. Classifying and counting foraminiferal shells is an important tool in assessing and reconstructing past and present environmental, oceanographic and climatological conditions. However, the present day manual identification procedure, performed with a microscope and a needle/brush, is a very time consuming. Circumventing this manual procedure, using machine leaning, promises to dramatically lower the time consumption related to generating foraminiferal data records.The first step towards that end is developing a deep learning model that can detect and classify microscopic foraminifera from 2D digital microscope pictures. The work is based on a VGG16 model implementation that has been pre trained on the ImageNet dataset and employing transfer learning techniques to adapt the model to the foraminifera task. The 2D photographic training data input was constructed by combining objects representative of and extracted from Arctic marine sediments (100µm-1mm size fraction) from the Barents Sea region. Four object groups, including 1) calcareous and 2) agglutinated benthic foraminifera, 3) planktic foraminifera and 4) sediments were used in the training data construction. With the initial set-up the algorithms were able to identify adherence to one of the four groups correctly ~90% of the time and with further fine-tuning and refinement reaching 98% correct identifications.The second step is to use machine leaning for classification of individual benthic calcareous foraminiferal species within the sediment. The work will focus on the 20 most common species that comprise ca. ≥ 80% of the total benthic calcareous foraminiferal fauna in the Arctic. The training of the algorithms will be done using targeted species-specific 2D photographic and 3D CT scanning data in addition to potentially using hyperspectral imaging.

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.