SplineDist: Automated Cell Segmentation With Spline Curves

Abstract

We present SplineDist, an instance segmentation convolutional neural network for bioimages extending the popular StarDist method. While StarDist describes objects as star-convex polygons, SplineDist uses a more flexible and general representation by modelling objects as planar parametric spline curves. Based on a new loss formulation that exploits the properties of spline constructions, we can incorporate our new object model in StarDist′s architecture with minimal changes. We demonstrate in synthetic and real images that SplineDist produces segmentation outlines of equal quality than StarDist with smaller network size and accurately captures non-star-convex objects that cannot be segmented with StarDist.

Squidpy: a scalable framework for spatial single cell analysis

“…Depending on the quality of the data, simple segmentation approaches like watershed might not be appropriate. Nowadays, many complex segmentation algorithms are provided as pre-trained deep learning models, such as Stardist 21 , Splinedist 22 and Cellpose 23 . These models can be easily used within the segmentation function.…”

Deep learning for bioimage analysis in developmental biology

“…(B) Fluorescence microscopy cell nuclei image from the Kaggle 2018 Data Science Bowl (dataset: BBBC038v1; Caicedo et al, 2019 ) segmented with StarDist ( Schmidt et al, 2018 ), in which objects are represented as star-convex polygons, and with SplineDist, in which objects are described as a planar spline curve. Image adapted from Mandal and Uhlmann (2021) . …”

“…light sheet microscopy) data, which is often generated in developmental biology experiments ( Weigert et al, 2020 ). More recently, SplineDist extends StarDist by using a more flexible representation of objects, allowing for the segmentation of more complex shapes ( Mandal and Uhlmann, 2021 ) ( Fig. 2 B).…”

A deep learning segmentation strategy that minimizes the amount of manually annotated images

“…Over the last decade, deep learning approaches have outperformed all existing methods for image segmentation [1][2][3][4] . Semantic segmentation, the estimation of a label at each pixel, and instance segmentation, the identification of individual objects, were successfully applied to spatially characterize biological entities in microscopic images [5][6][7][8] . However, these powerful approaches rely on large annotated datasets.…”