A computer vision framework for quantification of feather growth patterns.
A computer vision framework for quantification of feather growth patterns.
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DOI：
10.3389/fbinf.2023.1073918
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hsu, Edward
中科院分区：
文献类型：
--
作者：
Thompson, Tyler N.;Vickrey, Anna;Shapiro, Michael D.;Hsu, Edward
羽毛生长模式是研究皮肤和表皮附属物发育的潜在基因组调节的重要解剖表型。然而，羽毛生长模式的表征以前依赖于手动检查和目视检查，这对于大样本量来说既是主观的，而且实际上是禁止的。在这里，我们报告了一种新的高通量技术，用于量化构成家鸽头冠的反向羽毛的位置和空间范围。鸽子羽毛生长模式的表型变异通过计算机断层扫描（CT）扫描呈现为点云。然后，我们开发了基于机器学习的特征提取技术来分离羽毛，并以定量、自动化和非侵入性的方式绘制皮肤上的生长模式。五只测试动物的结果与通过目视检查获得的“地面真实”结果非常一致，这证明了这种方法量化羽毛生长模式的可行性。我们的研究结果强调了现代计算机视觉和机器学习技术在有机生物学和遗传学领域的潜力和日益不可或缺的作用。
Feather growth patterns are important anatomical phenotypes for investigating the underlying genomic regulation of skin and epidermal appendage development. However, characterization of feather growth patterns previously relied on manual examination and visual inspection, which are both subjective and practically prohibitive for large sample sizes. Here, we report a new high-throughput technique to quantify the location and spatial extent of reversed feathers that comprise head crests in domestic pigeons. Phenotypic variation in pigeon feather growth patterns were rendered by computed tomography (CT) scans as point clouds. We then developed machine learning based, feature extraction techniques to isolate the feathers, and map the growth patterns on the skin in a quantitative, automated, and non-invasive way. Results from five test animals were in excellent agreement with “ground truth” results obtained via visual inspection, which demonstrates the viability of this method for quantification of feather growth patterns. Our findings underscore the potential and increasingly indispensable role of modern computer vision and machine learning techniques at the interface of organismal biology and genetics.
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DOI：
10.1126/science.1230422
发表时间：
2013-03-01
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Shapiro MD;Kronenberg Z;Li C;Domyan ET;Pan H;Campbell M;Tan H;Huff CD;Hu H;Vickrey AI;Nielsen SC;Stringham SA;Hu H;Willerslev E;Gilbert MT;Yandell M;Zhang G;Wang J
通讯作者：
Wang J
影响因子：
2.1
作者：
Roller，Lauren A.;Chebib，Ivan;Chang，Connie Y.
通讯作者：
Chang，Connie Y.
DOI：
10.2106/00004623-200005000-00007
发表时间：
2000-05-01
影响因子：
5.3
作者：
Widhe, B;Widhe, T
通讯作者：
Widhe, T
影响因子：
2.7
作者：
Jones, Marc E. H.;Button, David J.;Porro, Laura B.
通讯作者：
Porro, Laura B.
影响因子：
2.7
作者：
Boer EF;Van Hollebeke HF;Shapiro MD
通讯作者：
Shapiro MD