Forabot: Automated Planktic Foraminifera Isolation and Imaging

Richmond, Turner; Cole, Jeremy; Dangler, Gabriella; Daniele, Michael A.; Marchitto, Thomas M.; Lobatón, Edgar

doi:10.1029/2022gc010689

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…However, this idea requires further exploration. Richmond et al [183] presents an open-source system that can physically manipulate individual foraminifera for imaging and isolation with minimal human interaction. The segmentation relies on a fine-tuned VGG-16 to accurately classify the fossil prior to segmentation.…”

Section: B Methods For Fossil Segmentationmentioning

confidence: 99%

Advancing Paleontology: A Survey on Deep Learning Methodologies in Fossil Image Analysis

Ansari,

Ishaq,

Yusuf

et al. 2024

Preprint

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Understanding ancient organisms and their paleo-environments through the study of body fossils represents a central tenet of paleontology. Advances in digital image capture over the past several decades now allow for efficient and accurate documentation, curation and interrogation of fossil anatomy over disparate length scales. Despite these developments, key body fossil image processing and analysis tasks, such as segmentation and classification still require significant user intervention, which can be labor-intensive and subject to human bias. Recent advancements in deep learning offer the potential to automate fossil image analysis while improving throughput and limiting operator bias. Despite the recent emergence of deep learning within paleontology, challenges such as the scarcity of diverse, high quality image datasets and the complexity of fossil morphology necessitate further advancements and the adoption of concepts from other scientific domains. Here, we comprehensively review state-of-the-art deep learning-based methodologies applied towards body fossil analysis while grouping the studies based on the fossil type and nature of the task. Furthermore, we analyze existing literature to tabulate dataset information, neural network architecture type, key results, and comprehensive textual summaries. Finally, based on the collective limitations of the existing studies, we discuss novel techniques for fossil data augmentation and fossil image enhancements, which can be combined with advanced neural network architectures, such as diffusion models, generative hybrid networks, transformers, and graph neural networks, to improve body fossil image analysis.

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Section: B Methods For Fossil Segmentationmentioning

confidence: 99%

Advancing Paleontology: A Survey on Deep Learning Methodologies in Fossil Image Analysis

Ansari,

Ishaq,

Yusuf

et al. 2024

Preprint

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“…Such methods involve taking photographs of sieved and washed foraminifera and using machine learning to subsequently classify the imaged foraminifera. This is done by defining hand-crafted features 14 or by learning the relevant features for classification based on the whole image of individual foraminifera [15][16][17] . Several efforts have incorporated 3D features by photographing specimens under different lighting conditions 14 , at different focal planes 18 or both 17 .…”

Section: Background and Summarymentioning

confidence: 99%

“…This is done by defining hand-crafted features 14 or by learning the relevant features for classification based on the whole image of individual foraminifera [15][16][17] . Several efforts have incorporated 3D features by photographing specimens under different lighting conditions 14 , at different focal planes 18 or both 17 . Despite these advances in automation, preparation of the sediment core sample (e.g.…”

Section: Background and Summarymentioning

confidence: 99%

ForametCeTera, a novel CT scan dataset to expedite classification research of (non-)foraminifera

Luijmes,

van Leeuwen,

Renema

2024

Sci Data

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This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are sensitive sentinels of environmental change and are a crucial component of carbonate-denominated ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in understanding climate change. However, analysis of foraminifera contained in core samples currently requires washing, sieving and manual quantification. These methods are thus time-consuming and require trained experts. To overcome these limitations, we propose an alternative workflow utilizing 3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources. Despite recent advancements in automation, a crucial lack of methods persists for segmenting and classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating synthetic digital core samples, facilitating the development of segmentation and classification methods of entire core sample CT scans.

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