分割
分辨率(逻辑)
计算机视觉
高分辨率
生物
模式识别(心理学)
计算生物学
超分辨率
活体细胞成像
作者
Adrian Wolny,Lorenzo Cerrone,Athul Vijayan,Rachele Tofanelli,Amaya Vilches Barro,Marion Louveaux,Christian Wenzl,Sören Strauss,David Wilson-Sánchez,Rena Lymbouridou,Susanne S Steigleder,Constantin Pape,Alberto Bailoni,Salva Duran-Nebreda,George W. Bassel,Jan U. Lohmann,Miltos Tsiantis,Fred A. Hamprecht,Kay Schneitz,Alexis Maizel,Anna Kreshuk
出处
期刊:eLife
[eLife Sciences Publications, Ltd.]
日期:2020-07-29
卷期号:9
被引量:60
摘要
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
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