Hirling, D. | bioRxiv Science

bioRxiv2024

When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling

Data is the driving engine of learning-based algorithms, the creation of which fundamentally determines the performance, accuracy, generalizability and quality of any model or method trained on it. When only skilled or trained personnel can create reliable annotations, assisted software solutions are desirable to reduce the time and effort the expert must spend on labelling. Herein is proposed an automated annotation helper software package in napari that offers multiple methods to assist the annotator in creating object-based labels on 2D or 3D images.

bioinformatics↗

bioRxiv2021

Fully Automatic Cell Segmentation with Fourier Descriptors

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.

bioinformatics↗