A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images

An optimized live imaging and multiple cell layer growth analysis approach using Arabidopsis sepals

Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope. To investigate how differential growth of connected cell layers generate unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal (or plant tissues in general) is practically challenging. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals, and subsequent image processing. For live imaging early-stage sepals, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX image processing software. We also describe the MorphoGraphX parameters for creating a 2.5D mesh surface for the inner epidermis. Our parameters allow for the segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. While we have used sepals to illustrate our approach, the methodology will be useful for researchers intending to live-image and track growth of deeper cell layers in 2.5D for any plant tissue.

An optimized live imaging and growth analysis approach for Arabidopsis Sepals

Background

Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis.

Results

For live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells.

Conclusion

We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.