WFRF:(Birgisdottir AB)
Sökning: WFRF:(Birgisdottir AB) > Physics-based machi...
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 02914naa a2200337 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:148345401 | |
| 003 | SwePub | |
| 008 | 240701s2021 | |||||||||||000 ||eng| | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1483454012 URI |
| 024 | 7 | a https://doi.org/10.1038/s42256-021-00420-02 DOI |
| 040 | a (SwePub)ki | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a vet2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Sekh, AA4 aut |
| 245 | 1 0 | a Physics-based machine learning for subcellular segmentation in living cells |
| 264 | c 2021-12-15 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2021 |
| 520 | a Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Therefore, manual segmentation relying on heuristics and experience remains the preferred approach. However, this process is tedious, given the countless structures present inside a single cell, and generating analytics across a large population of cells or performing advanced artificial intelligence tasks such as tracking are greatly limited. Here we bring modelling and deep learning to a nexus for solving this GT-hard problem, improving both the accuracy and speed of subcellular segmentation. We introduce a simulation-supervision approach empowered by physics-based GT, which presents two advantages. First, the physics-based GT resolves the GT-hardness. Second, computational modelling of all the relevant physical aspects assists the deep learning models in learning to compensate, to a great extent, for the limitations of physics and the instrument. We show extensive results on the segmentation of small vesicles and mitochondria in diverse and independent living- and fixed-cell datasets. We demonstrate the adaptability of the approach across diverse microscopes through transfer learning, and illustrate biologically relevant applications of automated analytics and motion analysis. | |
| 700 | 1 | a Opstad, IS4 aut |
| 700 | 1 | a Godtliebsen, G4 aut |
| 700 | 1 | a Birgisdottir, AB4 aut |
| 700 | 1 | a Ahluwalia, BSu Karolinska Institutet4 aut |
| 700 | 1 | a Agarwal, K4 aut |
| 700 | 1 | a Prasad, DK4 aut |
| 710 | 2 | a Karolinska Institutet4 org |
| 773 | 0 | t NATURE MACHINE INTELLIGENCEd : Springer Science and Business Media LLCg 3:12, s. 1071-1080q 3:12<1071-1080x 2522-5839 |
| 856 | 4 | u https://www.nature.com/articles/s42256-021-00420-0.pdf |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:148345401 |
| 856 | 4 8 | u https://doi.org/10.1038/s42256-021-00420-0 |
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