Leveraging Computer Vision for Applications in Biomedicine and Geoscience

Skin cancer is one of the most common types of cancer and is usually classified as either non-melanoma and melanoma skin cancer. Melanoma skin cancer accounts for about half of all skin cancer-related deaths. The 5-year survival rate is 99% when the cancer is detected early but drops to 25% once it becomes metastatic. In other words, the key to preventing death is early detection. Foraminifera are microscopic single-celled organisms that exist in marine environments and are classified as living a benthic or planktic lifestyle. In total, roughly 50,000 species are known to have existed, of which about 9,000 are still living today. Foraminifera are important proxies for reconstructing past ocean and climate conditions and as bio-indicators of anthropogenic pollution. Since the 1800s, the identification and counting of foraminifera have been performed manually. The process is resource-intensive. In this dissertation, we leverage recent advances in computer vision, driven by breakthroughs in deep learning methodologies and scale-space theory, to make progress towards both early detection of melanoma skin cancer and automation of the identification and counting of microscopic foraminifera. First, we investigate the use of hyperspectral images in skin cancer detection by performing a critical review of relevant, peer-reviewed research. Second, we present a novel scale-space methodology for detecting changes in hyperspectral images. Third, we develop a deep learning model for classifying microscopic foraminifera. Finally, we present a deep learning model for instance segmentation of microscopic foraminifera. The works presented in this dissertation are valuable contributions in the fields of biomedicine and geoscience, more specifically, towards the challenges of early detection of melanoma skin cancer and automation of the identification, counting, and picking of microscopic foraminifera

Multimodal Image and Spectral Feature Learning for Efficient Analysis of Water-Suspended Particles

apan Science and Technology Agency SICORP and Natural Environment Research Council (JST-NERC SICORP Marine Sensor Proof of Concept Grant JPMJSC1705, NE/R01227X/1); JSPS KAKENHI Grant (18K13934 and 18H03810); Sumitomo Foundation: Grant for environmental Research Project (203122). Acknowledgments. The authors thank Dr. T. Fukuba for the support for building the experimental setup. The authors also thank Dr. H. Sawada for providing samples for this work.Peer reviewedPublisher PD

Modern fringing reef carbonates from equatorial SE Asia: An integrated environmental, sediment and satellite characterisation study.

Fringing reefs of SE Asia may conservatively comprise ~30% of the world’s coral reef area, but remain almost unstudied (White, 1987; Tomascik et al., 1997). This study provides insights into the primary sedimentological and early alteration characteristics of an isolated fringing reef system (Kaledupa-Hoga) from the Tukang Besi Archipelago, SE Asia. A combined multispectral satellite imagery, field and petrographic study allowed for the generation of an environmental facies map, which acts as a model for the distribution of primary sedimentological characteristics in relation to the primary environmental facies. The islands of the Tukang Besi Archipelago are mesotidal (<2 m) affected by strong diurnal and oceanic tidal currents, as well as high wave energy influenced by the bi-directional southeast Asian monsoon. An environmental facies map generated from Landsat-7 imagery and utilising field observations defines ten environmental facies. The facies map generated has a >71% accuracy when compared with field and sedimentary data. With the exception of the reef crest and reef slope that commonly have widths on a sub-imaging resolution (<30 m), the facies map accurately demonstrates the heterogeneous nature of the carbonate system. Although field and satellite imagery observations reveal ten environmental facies, sedimentological characterisation results in a lower number of distinctive categories due to the similarity of many deposits. Foreshore/backshore and bare intertidal deposits are distinctive and are composed of reef-derived material that has been reworked shorewards. Seagrass-associated facies all show some fine silt-clay sized material (<8%) with common imperforate foraminifera and pervasive micritisation, but also contain high abundances of reworked coral and shell allochems. Coral-associated reef flat facies are typically low in imperforate but high in perforate foraminifera, and show lesser effects of bioerosion and very low silt contents. The reef slope and crest are characterised by high abundances of gravel-sized fragmented corals with the highest abundances of echinoderm material and alcyonarian sclerites. Sediment samples across all fringing reef environments from the Kaledupa-Hoga transects are characterised almost exclusively by grain-rudstone textures, with <2-5% silt and clay size fractions, and minor baffling of fines in seagrass-associated settings (grain-packstones). The paucity of fines across the fringing reef systems as a whole, and the degree of homogenisation of sediment characteristics across the different field- and satellite-identifiable environmental facies are attributed to: (1) high wave/current energies, (2) the small size of the islands rendering limited protection, (3) bidirectional monsoon winds and (4) the lack of reef rimmed margins built to sea level. Absent from these deposits are well developed high energy windward and low energy leeward deposit characteristics and/or an overriding hurricane influence that are commonly seen in fringing reef systems from other areas

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Digital In-Line Holography for Large-Volume Analysis of Vertical Motion of Microscale Marine Plankton and Other Particles

Acknowledgements This work is funded by a joint UK-Japan research program (NERC-JST SICORP Marine Sensor Proof of Concept under project code NE/R01227X/1). The authors would like to thank the captain, crew, science party and technical support staff of the R/V Yokosuka cruise YK20-E02. We also thank Dr. Y. Nagai for providing us the foraminifera samples.Peer reviewedPostprin

A NEW MODEL ON BENTHIC FORAMINIFER IMAGE CLASSIFICATION AND DEFINITIONS BASED ON CONVENTIONAL NEURAL NETWORK (CNN)

Fossil studies are of great importance in order to observe the change of living species over the years, to make inferences by using the information provided by the observed species, and to understand the developing and changing structure of the world we live in over the years. However, the examination and interpretation of fossil specimens is a complex and long process. Artificial intelligence studies have begun to be applied to this field in order to facilitate the working methods of paleontologists. The detection and classification of fossil specimens with the aid of computers simplifies this process as much as possible compared to manual classification processes and reduces foreign dependency for fossil assemblages for which paleontologists are not experts. To achieve this, 9 benthic foraminiferal species and non-foraminiferal sample photographs from a selected dataset were used. In this study, a new method developed for the classification of benthic foraminifera using deep convolutional neural networks, reaching higher accuracy than the results in the literature, is presented. With this method, at least 70% accuracy rates were achieved in the test results of the trained system. This study, which reached high accuracy rates with a new method, has created a successful development for the branch of paleontology in the use of artificial intelligence in microfossil identification

PORIFERAL VISION: Deep Transfer Learning-based Sponge Spicules Identification & Taxonomic Classification

The phylum Porifera includes the aquatic organisms known as sponges. Sponges are classified into four classes: Calcarea, Hexactinellida, Demospongiae, and Homoscleromorpha. Within Demospongiae and Hexactinellida, sponges’ skeletons are needle-like spicules made of silica. With a wide variety of shapes and sizes, these siliceous spicules’ morphology plays a pivotal role in assessing and understanding sponges\u27 taxonomic diversity and evolution. In marine ecosystems, when sponges die their bodies disintegrate over time, but their spicules remain in the sediments as fossilized records that bear ample taxonomic information to reconstruct the evolution of sponge communities and sponge phylogeny. Traditional methods of identifying spicules from core samples of marine sediments are labor-intensive and cannot scale to the scope needed for large analysis. Through the incorporation of high-throughput microscopy and deep learning, image classification has made significant strides toward automating the task of species recognition and taxonomic classification. Even with sparse training data and highly specific image domains, deep convolutional neural networks (DCNNs) were able to extract taxonomic features among morphologically diverse microfossils. Using transfer learning, training a classifier on pretrained DCNNs has achieved recent successes in classifying similar microfossils, such as diatom frustules and radiolarian skeletons. In this project, I address the reliability of pretrained models to perform spicule identification and class-level classification. Using FlowCam technology to photograph individual microparticles, our dataset consists of spicule and non-spicule types without additional image segmentation and augmentation. Our proposed method is a pre-trained model with a custom classifier that performs two different binary classifications: a spicule vs non-spicule classification, and a taxonomic classification of Demospongiae vs. Hexactinellida. We evaluate the effect of implementing different DCNN architectures, data set sizes, and classifiers on image classification performance. Surprisingly, MobileNet, a relatively new and small architecture, showed the best performance while still being the most computationally efficient. Other studies that didn’t involve MobileNet had similar high accuracies for multi-class classifications with fewer training images. The reliability of DCNNs for binary spicule classification implicates the promising approach of a more nuanced multi-class/taxonomic classification. Future work should build multi-class classification that ranges more biogenic materials for the identification or more sponge taxonomic levels for species classification

Multimodal image and spectral feature learning for efficient analysis of water-suspended particles

We have developed a method to combine morphological and chemical information for the accurate identification of different particle types using optical measurement techniques that require no sample preparation. A combined holographic imaging and Raman spectroscopy setup is used to gather data from six different types of marine particles suspended in a large volume of seawater. Unsupervised feature learning is performed on the images and the spectral data using convolutional and single-layer autoencoders. The learned features are combined, where we demonstrate that non-linear dimensional reduction of the combined multimodal features can achieve a high clustering macro F1 score of 0.88, compared to a maximum of 0.61 when only image or spectral features are used. The method can be applied to long-term monitoring of particles in the ocean without the need for sample collection. In addition, it can be applied to data from different types of sensor measurements without significant modifications

A seismic-driven 3D model of rock mechanical facies: An example from the Asmari reservoir, SW Iran

Asmari Formation is one of the most prolific and important hydrocarbon reservoirs in Iran. This formation in the Cheshmeh-Khosh oilfield shows mixed carbonate-siliciclastic lithology and its elastic modulus changes are correlatable with facies changes. To address these changes, we investigated the relation between sedimentary environment (facies) and texture with various elastic moduli. The Young's modulus shows higher correlation with the facies changes. Data from three wells are analyzed and used for the construction of rock mechanical facies. Based on elastic properties, facies and texture changes as well as petrophysical characteristics seven rock mechanical facies (RMFs) are recognized in the studied formation. To predict RMFs at inter-well spaces more efficiently and capturing the lateral formation property variationsa 3D rock mechanical facies model is constructed based on seismic attributes. In this method, RMFs are correlatable between the studied wells and mappable by seismic attribute in the field scale. Finally, the distribution of RMFs and their related properties is investigated in the studied field