- Titel
- Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-896305
- Quellenangabe
- Scientific reports
Erscheinungsjahr: 2022
Jahrgang: 12
E-ISSN: 2045-2322
Artikelnummer: 18846 - Erstveröffentlichung
- 2022
- Abstract (EN)
- Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery.
- Andere Ausgabe
- Link zum Artikel der zuerst in der Zeitschrift „Scientific reports” bei Springer Nature erschienen ist.
DOI: 10.1038/s41598-022-23490-5 - Freie Schlagwörter (DE)
- Krebsbildgebung, Mikroendoskopie, Bildgebung, Sensorik
- Freie Schlagwörter (EN)
- Cancer imaging, Microendoscopy, Imaging, sensing
- Klassifikation (DDC)
- 500
- 600
- Verlag
- Macmillan Publishers Limited, part of Springer Nature, London
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
ID: Cz55/47-1 - Deutsche Forschungsgemeinschaft (DFG)
ID: Cz55/48-1 - Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-896305
- Veröffentlichungsdatum Qucosa
- 01.03.2024
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0