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

Hou, Yining; Canul-Ku, Mario; Cui, Xindong; Hasimoto-Beltrán, Rogelio; Zhu, Min

doi:10.5194/jm-40-163-2021

Cited by 9 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address these limitations, contemporary solutions harness the power of DL. Similarly, DL also finds application in extracting distinct parts of biological anatomy, utilising either semantic segmentation techniques (Hou et al, 2021) or well-crafted training sets (Lösel et al, 2023), even in scenarios where density values closely resemble each other (Case Study 4). Although these methods currently require some manual intervention, they undeniably reduce processing times substantially.…”

Section: ____________________________________________________________...mentioning

confidence: 99%

Challenges and opportunities in applying AI to evolutionary morphology

He,

Mulqueeney,

Watt

et al. 2024

Preprint

View full text Add to dashboard Cite

Artificial intelligence (AI) is poised to transform many aspects of society, and the study of evolutionary morphology is no exception. Machine learning-grade methods of AI such as Principal Component Analysis (PCA) and Cluster Analysis have been commonplace in evolutionary morphology for decades, but the last decade has seen increasing application of Deep Learning to ecology and evolutionary biology, opening up the potential to circumvent longstanding barriers to rapid, big data analysis of phenotype. Here we review the current state of AI methods available for the study of evolutionary morphology and discuss the prospectus for near-term advances in specific subfields of this research area, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. We introduce the main available AI techniques, categorising them into three stages based on their order of appearance: (i) Machine Learning, (ii) Deep Learning with neural networks and (iii) the most recent advancements in large-scale models and multimodal learning. Next, we present existing AI approaches and case studies using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, phylogenetics, and biomechanics. Finally, we discuss areas where there is potential, but no current application of AI to key areas in evolutionary morphology. Combined, these advancements and potential developments have the capacity to transform the evolutionary analysis of organismal phenotype into evolutionary phenomics, launch it fully in the “Big Data'' sphere, and align it with genomics and other areas of bioinformatics.

show abstract

Section: ____________________________________________________________...mentioning

confidence: 99%

Challenges and opportunities in applying AI to evolutionary morphology

He,

Mulqueeney,

Watt

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is beneficial to obtain accurate image segmentation results. 25 To obtain high-resolution CT images, paleontologists usually purchase more advanced CT equipment. This method is not available in some scenarios.…”

Section: Introductionmentioning

confidence: 99%

Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Micropaleontologists use the fine structures of microfossils to extract evolutionary information. These structures could not be directly observed with the naked eye. Recently, paleontologists resort to computed tomography (CT) images to mine the information, and pursue higher resolution CT images with in‐depth research. Therefore, we propose a new model, weighted super‐resolution generative adversarial network (WSRGAN), for the super‐resolution reconstruction of CT images. The model proposed herein (WSRGAN) obtained higher LPIPS (0.0757) on the experimental dataset, compared with Bilinear (0.4289), Bicubic (0.4166), EDSR (0.2281), WDSR (0.2640), and SRGAN (0.0815). WSRGAN meets the requirements of paleontologists for reconstructing fish microfossils. We hope that more super‐resolution reconstruction methods based on deep learning could be applied to paleontology and achieve better performance.

show abstract

“…Cultural heritage research has used CT imaging of artifacts to determine their manufacturing process 4 , current state [5][6][7] , and origin 8 . Over the past years, the possibilities of CT imaging have been expanded by applying image processing methods to CT data, for example when unfolding unopened documents 9 , combining CT data with other 3D imaging methods 10 , and applying deep learning techniques to improve resolution 11 .…”

mentioning

confidence: 99%

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums

Bossema,

Palenstijn,

Heginbotham

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Visualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications. We propose an approach for creating accurate CT reconstructions using only standard 2D radiography equipment already available in most larger museums. Specifically, we demonstrate that a combination of basic X-ray imaging equipment, a tailored marker-based image acquisition protocol, and sophisticated data-processing algorithms, can achieve 3D imaging of collection objects without the need for a costly CT imaging system. We implemented this approach in the British Museum (London), the J. Paul Getty Museum (Los Angeles), and the Rijksmuseum (Amsterdam). Our work paves the way for broad facilitation and adoption of CT technology across museums worldwide.

show abstract

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

Cited by 9 publications

References 51 publications

Challenges and opportunities in applying AI to evolutionary morphology

Challenges and opportunities in applying AI to evolutionary morphology

Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums

Contact Info

Product

Resources

About