Towards the analysis of coral skeletal density-banding using deep learning

Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identifi...
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Autor*in:	Rutterford, Ainsley [verfasserIn] Bertini, Leonardo Hendy, Erica J. Johnson, Kenneth G. Summerfield, Rebecca Burghardt, Tilo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Coral density banding Extension rate Calcification rate Artificial intelligence X-ray micro-computed tomography

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 4(2022), 2 vom: 04. Jan.
Übergeordnetes Werk:	volume:4 ; year:2022 ; number:2 ; day:04 ; month:01

Links:	Volltext

DOI / URN:	10.1007/s42452-021-04912-x

Katalog-ID:	SPR045884943

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520			\|a Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI.
520			\|a Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments.
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allfields	10.1007/s42452-021-04912-x doi (DE-627)SPR045884943 (SPR)s42452-021-04912-x-e DE-627 ger DE-627 rakwb eng Rutterford, Ainsley verfasserin aut Towards the analysis of coral skeletal density-banding using deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213 Bertini, Leonardo (orcid)0000-0003-3920-4476 aut Hendy, Erica J. (orcid)0000-0001-5949-4349 aut Johnson, Kenneth G. (orcid)0000-0002-4666-1213 aut Summerfield, Rebecca aut Burghardt, Tilo aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 4(2022), 2 vom: 04. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:4 year:2022 number:2 day:04 month:01 https://dx.doi.org/10.1007/s42452-021-04912-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 2 04 01
spelling	10.1007/s42452-021-04912-x doi (DE-627)SPR045884943 (SPR)s42452-021-04912-x-e DE-627 ger DE-627 rakwb eng Rutterford, Ainsley verfasserin aut Towards the analysis of coral skeletal density-banding using deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213 Bertini, Leonardo (orcid)0000-0003-3920-4476 aut Hendy, Erica J. (orcid)0000-0001-5949-4349 aut Johnson, Kenneth G. (orcid)0000-0002-4666-1213 aut Summerfield, Rebecca aut Burghardt, Tilo aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 4(2022), 2 vom: 04. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:4 year:2022 number:2 day:04 month:01 https://dx.doi.org/10.1007/s42452-021-04912-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 2 04 01
allfields_unstemmed	10.1007/s42452-021-04912-x doi (DE-627)SPR045884943 (SPR)s42452-021-04912-x-e DE-627 ger DE-627 rakwb eng Rutterford, Ainsley verfasserin aut Towards the analysis of coral skeletal density-banding using deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213 Bertini, Leonardo (orcid)0000-0003-3920-4476 aut Hendy, Erica J. (orcid)0000-0001-5949-4349 aut Johnson, Kenneth G. (orcid)0000-0002-4666-1213 aut Summerfield, Rebecca aut Burghardt, Tilo aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 4(2022), 2 vom: 04. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:4 year:2022 number:2 day:04 month:01 https://dx.doi.org/10.1007/s42452-021-04912-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 2 04 01
allfieldsGer	10.1007/s42452-021-04912-x doi (DE-627)SPR045884943 (SPR)s42452-021-04912-x-e DE-627 ger DE-627 rakwb eng Rutterford, Ainsley verfasserin aut Towards the analysis of coral skeletal density-banding using deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213 Bertini, Leonardo (orcid)0000-0003-3920-4476 aut Hendy, Erica J. (orcid)0000-0001-5949-4349 aut Johnson, Kenneth G. (orcid)0000-0002-4666-1213 aut Summerfield, Rebecca aut Burghardt, Tilo aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 4(2022), 2 vom: 04. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:4 year:2022 number:2 day:04 month:01 https://dx.doi.org/10.1007/s42452-021-04912-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 2 04 01
allfieldsSound	10.1007/s42452-021-04912-x doi (DE-627)SPR045884943 (SPR)s42452-021-04912-x-e DE-627 ger DE-627 rakwb eng Rutterford, Ainsley verfasserin aut Towards the analysis of coral skeletal density-banding using deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213 Bertini, Leonardo (orcid)0000-0003-3920-4476 aut Hendy, Erica J. (orcid)0000-0001-5949-4349 aut Johnson, Kenneth G. (orcid)0000-0002-4666-1213 aut Summerfield, Rebecca aut Burghardt, Tilo aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 4(2022), 2 vom: 04. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:4 year:2022 number:2 day:04 month:01 https://dx.doi.org/10.1007/s42452-021-04912-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 2 04 01
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author	Rutterford, Ainsley
spellingShingle	Rutterford, Ainsley misc Coral density banding misc Extension rate misc Calcification rate misc Artificial intelligence misc X-ray micro-computed tomography Towards the analysis of coral skeletal density-banding using deep learning
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topic_title	Towards the analysis of coral skeletal density-banding using deep learning Coral density banding (dpeaa)DE-He213 Extension rate (dpeaa)DE-He213 Calcification rate (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 X-ray micro-computed tomography (dpeaa)DE-He213
topic	misc Coral density banding misc Extension rate misc Calcification rate misc Artificial intelligence misc X-ray micro-computed tomography
topic_unstemmed	misc Coral density banding misc Extension rate misc Calcification rate misc Artificial intelligence misc X-ray micro-computed tomography
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title	Towards the analysis of coral skeletal density-banding using deep learning
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title_sort	towards the analysis of coral skeletal density-banding using deep learning
title_auth	Towards the analysis of coral skeletal density-banding using deep learning
abstract	Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. © The Author(s) 2021
abstractGer	Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. © The Author(s) 2021
abstract_unstemmed	Abstract X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI. Article Highlights AI can help facilitate the expert identification of coral density boundaries in well-resolved regions following minimal training.AI can be used to automate and upscale non-destructive coral density-banding analysis across museum collections.Holistic analyses of coral density banding are central to understand coral growth responses to changing environments. © The Author(s) 2021
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title_short	Towards the analysis of coral skeletal density-banding using deep learning
url	https://dx.doi.org/10.1007/s42452-021-04912-x
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author2	Bertini, Leonardo Hendy, Erica J. Johnson, Kenneth G. Summerfield, Rebecca Burghardt, Tilo
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up_date	2024-07-03T18:55:49.489Z
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However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. 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Towards the analysis of coral skeletal density-banding using deep learning

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