Single Tensor Cell Segmentation using Scalar Field Representations
This work addresses cell segmentation for biomedical imaging, offering an incremental improvement through a geometrically insightful method that enhances efficiency for edge computing applications.
The authors tackled cell image segmentation by learning a continuous scalar field parameterized by a network, using solutions to Poisson and heat diffusion equations to achieve robust regression and sharp boundaries. They reported competitive results on public datasets with simplified implementation, lower training/inference times, and reduced energy consumption.
We investigate image segmentation of cells under the lens of scalar fields. Our goal is to learn a continuous scalar field on image domains such that its segmentation produces robust instances for cells present in images. This field is a function parameterized by the trained network, and its segmentation is realized by the watershed method. The fields we experiment with are solutions to the Poisson partial differential equation and a diffusion mimicking the steady-state solution of the heat equation. These solutions are obtained by minimizing just the field residuals, no regularization is needed, providing a robust regression capable of diminishing the adverse impacts of outliers in the training data and allowing for sharp cell boundaries. A single tensor is all that is needed to train a \unet\ thus simplifying implementation, lowering training and inference times, hence reducing energy consumption, and requiring a small memory footprint, all attractive features in edge computing. We present competitive results on public datasets from the literature and show that our novel, simple yet geometrically insightful approach can achieve excellent cell segmentation results.