Spherical Harmonics for Shape-Constrained 3D Cell Segmentation
| Autor: | Dennis Eschweiler, Simon Koppers, Malte Rethwisch, Johannes Stegmaier |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Artificial neural network business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Process (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Spherical harmonics Pattern recognition Image segmentation Cell morphology Set (abstract data type) Segmentation Artificial intelligence business Representation (mathematics) |
| Zdroj: | ISBI |
| DOI: | 10.48550/arxiv.2010.12369 |
| Popis: | Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of image data generated by current digitized imaging techniques, automated approaches are demanded more than ever. Segmentation approaches used for morphological analyses, however, are often prone to produce unnaturally shaped predictions, which in conclusion could lead to inaccurate experimental outcomes. In order to minimize further manual interaction, shape priors help to constrain the predictions to the set of natural variations. In this paper, we show how spherical harmonics can be used as an alternative way to inherently constrain the predictions of neural networks for the segmentation of cells in 3D microscopy image data. Benefits and limitations of the spherical harmonic representation are analyzed and final results are compared to other state-of-the-art approaches on two different data sets. |
| Databáze: | OpenAIRE |
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