LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Computer Graphics Forum Year : 2022

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Abstract

We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.
Fichier principal
Vignette du fichier
Hong_2022_CST.pdf (12.06 Mo) Télécharger le fichier
Vignette du fichier
Hong_2022_CST (1).jpg (10.75 Ko) Télécharger le fichier
Vignette du fichier
Hong_2022_CST.jpg (10.75 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Format Figure, Image
Licence
Format Figure, Image
Licence

Dates and versions

hal-03699221 , version 1 (20-06-2022)

Identifiers

Cite

Jiayi Hong, Alain Trubuil, Tobias Isenberg. LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning. Computer Graphics Forum, 2022, 41 (3), pp.195--207. ⟨10.1111/cgf.14533⟩. ⟨hal-03699221⟩
149 View
165 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More