Sökning: id:"swepub:oai:DiVA.org:uu-523993" >
Cell Segmentation o...
Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning
-
- Andersson, Axel (författare)
- Uppsala universitet,Avdelningen Vi3,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab
-
- Behanova, Andrea (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Wählby, Carolina, professor, 1974- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen Vi3
-
visa fler...
-
- Malmberg, Filip, 1980- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Science for Life Laboratory, SciLifeLab
-
visa färre...
-
(creator_code:org_t)
- Cham : Springer, 2023
- 2023
- Engelska.
-
Ingår i: Graph-Based Representations in Pattern Recognition. - Cham : Springer. - 9783031427947 - 9783031427954 ; , s. 139-148
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Cell segmentation
- in situ transcriptomics
- tissue analysis
- mutex watershed
- Computerized Image Processing
- Datoriserad bildbehandling
- Machine learning
- Maskininlärning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas