Using pose estimation to identify regions and points on natural history specimens.
Using pose estimation to identify regions and points on natural history specimens.
复制标题
DOI：
10.1371/journal.pcbi.1010933
复制
发表时间：
2023-02
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
动员越来越多的数字化生物标本用于科学研究的一个关键挑战是找到高通量方法来提取这些数据集的表型测量。在本文中，我们测试了一种基于深度学习的姿态估计方法，该方法能够准确地放置点标签以识别样本图像上的关键位置。然后，我们将该方法应用于两个不同的挑战，每个挑战都需要识别2D图像中的关键特征：(i)识别鸟类标本上特定身体区域的羽毛颜色，（ii）测量Littorina蜗牛壳的形态变化。对于鸟类数据集，95%的图像被正确标记，从这些预测点得出的颜色测量值与基于人类的测量值高度相关。对于Littorina数据集，超过95%的地标相对于专家标记的地标被准确地放置，并且预测的地标可靠地捕获了两种不同贝壳生态型（“螃蟹”与“波浪”）之间的形状变化。总的来说，我们的研究表明，基于深度学习的姿态估计可以为数字化的基于图像的生物多样性数据集生成高质量和高通量的基于点的测量结果，并可能标志着这些数据的动员发生了一步变化。我们还提供了在大规模生物数据集上使用姿态估计方法的一般指南。随着自然历史收藏的数字化继续快速发展，大量的信息正等待着从这些庞大的数字数据集中被调动起来，这些信息可以帮助解决许多进化和生态问题。深度学习已经在许多现实世界的任务上取得了成功，比如人脸识别和图像分类。在这里，我们通过在鸟类和长春花的照片上放置点来使用深度学习来测量标本的表型特征。我们表明，深度学习产生的测量通常是准确的，与专家进行的手动测量非常相似。由于深度学习方法大大减少了产生这些测量所需的时间，我们的研究结果证明了深度学习在未来生物多样性研究中的巨大潜力。
A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes (‘crab’ vs ‘wave’). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets. As the digitisation of natural history collections continues apace, a wealth of information is waiting to be mobilised from these vast digital datasets that can help address many evolutionary and ecological questions. Deep Learning has achieved success on many real-world tasks such as face recognition and image classification. Here, we use deep learning to measure phenotypic traits of specimens by placing points on photos of birds and periwinkles. We show that the measurements produced by Deep Learning are generally accurate and very similar to manual measurements taken by experts. As Deep Learning methods vastly reduce the time required to produce these measurements, our results demonstrate the great potential of Deep Learning for future biodiversity studies.
登录
查看更多内容
影响因子：
8.8
作者：
Chira AM;Cooney CR;Bright JA;Capp EJR;Hughes EC;Moody CJA;Nouri LO;Varley ZK;Thomas GH
通讯作者：
Thomas GH
DOI：
10.1126/science.1253451
发表时间：
2014-12-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jarvis ED;Mirarab S;Aberer AJ;Li B;Houde P;Li C;Ho SY;Faircloth BC;Nabholz B;Howard JT;Suh A;Weber CC;da Fonseca RR;Li J;Zhang F;Li H;Zhou L;Narula N;Liu L;Ganapathy G;Boussau B;Bayzid MS;Zavidovych V;Subramanian S;Gabaldón T;Capella-Gutiérrez S;Huerta-Cepas J;Rekepalli B;Munch K;Schierup M;Lindow B;Warren WC;Ray D;Green RE;Bruford MW;Zhan X;Dixon A;Li S;Li N;Huang Y;Derryberry EP;Bertelsen MF;Sheldon FH;Brumfield RT;Mello CV;Lovell PV;Wirthlin M;Schneider MP;Prosdocimi F;Samaniego JA;Vargas Velazquez AM;Alfaro-Núñez A;Campos PF;Petersen B;Sicheritz-Ponten T;Pas A;Bailey T;Scofield P;Bunce M;Lambert DM;Zhou Q;Perelman P;Driskell AC;Shapiro B;Xiong Z;Zeng Y;Liu S;Li Z;Liu B;Wu K;Xiao J;Yinqi X;Zheng Q;Zhang Y;Yang H;Wang J;Smeds L;Rheindt FE;Braun M;Fjeldsa J;Orlando L;Barker FK;Jønsson KA;Johnson W;Koepfli KP;O'Brien S;Haussler D;Ryder OA;Rahbek C;Willerslev E;Graves GR;Glenn TC;McCormack J;Burt D;Ellegren H;Alström P;Edwards SV;Stamatakis A;Mindell DP;Cracraft J;Braun EL;Warnow T;Jun W;Gilbert MT;Zhang G
通讯作者：
Zhang G
DOI：
10.1111/evo.12329
发表时间：
2014-04
期刊：
Evolution; international journal of organic evolution
影响因子：
--
作者：
Butlin RK;Saura M;Charrier G;Jackson B;André C;Caballero A;Coyne JA;Galindo J;Grahame JW;Hollander J;Kemppainen P;Martínez-Fernández M;Panova M;Quesada H;Johannesson K;Rolán-Alvarez E
通讯作者：
Rolán-Alvarez E
DOI：
10.1098/rspb.2021.0919
发表时间：
2021-07-14
期刊：
Proceedings. Biological sciences
影响因子：
--
作者：
Felice RN;Pol D;Goswami A
通讯作者：
Goswami A
影响因子：
64.8
作者：
Cooney CR;Bright JA;Capp EJR;Chira AM;Hughes EC;Moody CJA;Nouri LO;Varley ZK;Thomas GH
通讯作者：
Thomas GH