ABSTRACT
Cell segmentation is a fundamental problem in biology for which convolutional neural networks yield the best results nowadays. In this paper, we present FourierDist, a network, which is a modification of the popular StarDist and SplineDist architectures. While StarDist and SplineDist describe an object by the lengths of equiangular rays and control points respectively, our network utilizes Fourier descriptors, predicting a coefficient vector for every pixel on the image, which implicitly define the resulting segmentation. We evaluate our model on three different datasets, and show that Fourier descriptors can achieve a high level of accuracy with a small number of coefficients. FourierDist is also capable of accurately segmenting objects that are not star-shaped, a case where StarDist performs suboptimally according to our experiments.
Competing Interest Statement
The authors have declared no competing interest.
