Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images

Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, Acute, with a companion homology workflow, Constellation, in the PlantCV environment. Acute identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. Constellation uses a strategy of dimensionality reduction via starscape followed by hierarchical clustering through constella to identify ‘constellations’ of segments in eigenspace ...全程评估植物表型（plant phenotypes）是解析形态发育、遗传机制与演化历程的核心环节，同时对农艺学及基础研究具有关键价值。尽管目前已开发出多种自动化或半自动化的表型组学（phenomic）研究方法，但由于难以分割并标注植物体内的特定结构或位置，且无法在时序数据中维持其身份标识，因此分析差异生长仍存在较大挑战。为填补这一研究空白，我们在PlantCV环境中开发了一套通用工作流，将此前发表的工具函数Acute与配套的同源分析工作流Constellation进行整合。其中，Acute可识别植物体内的锐角形态（伪地标，pseudo-landmarks），其对应位点多为叶尖与叶舌区域；Constellation则采用先通过starscape进行降维、再通过constella执行层级聚类的策略，以识别特征空间（eigenspace）中的"星群"（constellations）……