Enhancing the morphological segmentation of microscopic fossils through Localized Topology-Aware Edge Detection

Ge, Qian; Richmond, Turner; Zhong, Boxuan; Marchitto, Thomas M.; Lobatón, Edgar

doi:10.1007/s10514-020-09950-9

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…In a similar study, Ge at al. [184] propose a homologybased detector of local structural difference between two fossil edge maps with a tolerable deformation. The proposed detector enhances the fossils' structural differences, which specialized loss functions use to assess 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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“…Though the existing works are quite successful, other works exist which aim to extract morphological characteristics such as a chamber segmentation (Ge et al, 2017(Ge et al, , 2021. While these features haven't been used in classification yet, they do provide an interesting extension in getting automated measurements of foram species.…”

Section: Foraminifera Classification Systemsmentioning

confidence: 99%

Forabot: Automated Planktic Foraminifera Isolation and Imaging

Richmond

Cole

Dangler

et al. 2022

Geochem Geophys Geosyst

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Foraminifera, or forams for short, are ubiquitous in the world ocean (Sengupta, 1999). Along with their abundance, their biodiversity and extent of geologic record make their fossils of particular interest to paleontologists and paleoclimatologists (Schmiedl, 2019). The sand-sized fraction of deep sea sediments is often dominated by planktic foraminifera, of which there are about 50 extant species (Schiebel & Hemleben, 2017). Although modern benthic foraminiferal species number in the thousands, they are typically only abundant in shallow (shelf) environments and in poorly preserved abyssal sediments. For this work, we focus on deep-sea sediment and as such do not include analysis or commentary on benthic foraminifera. Due to the small size and great abundance of planktic foraminifera, hundreds or possibly thousands can often be picked from a single cubic centimeter of ocean floor mud. Foraminifera samples are normally sorted by species before they are used for either academic or

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“…U-Net is a classic network for semantic segmentation that performs well in microfossil images, especially CT data. The U-Net model has been successfully utilized for planktonic foraminifera recognition (Carvalho et al, 2020;Ge et al, 2020), charcoal particle identification (Rehn et al, 2019), and other micropaleontology tasks. In this paper, we chose an improved U-Net model to semantically segment fish microfossils from CT data.…”

Section: Network Structurementioning

confidence: 99%

Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

Hou

Canul-Ku

Cui

et al. 2021

J. Micropalaeontol.

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Abstract. Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.

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Enhancing the morphological segmentation of microscopic fossils through Localized Topology-Aware Edge Detection

Cited by 8 publications

References 42 publications

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

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

Forabot: Automated Planktic Foraminifera Isolation and Imaging

Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

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