ArticleFrontiers in neuroscience2020
Spectrally Tunable Neural Network-Assisted Segmentation of Microneurosurgical Anatomy.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.Journal of biomedical optics · 2025Review
- Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review.Frontiers in surgery · 2022Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDistinct tissue types are differentiated based on the surgeon's knowledge and subjective visible information, typically assisted with white-light intraoperative imaging systems. Narrow-band imaging (NBI) assists in tissue identification and enables automated classifiers, but many anatomical details moderate computational predictions and cause bias. In particular, tissues' light-source-dependent optical characteristics, anatomical location, and potentially hazardous microstructural changes such as peeling have been overlooked in previous literature.
methodsNarrow-band images of five (
resultsReflectance between intact and peeled arteries in lower-wavelength domains between 400 and 576 nm was significantly different (
conclusionSelective NBI represents a promising method for assisting tissue identification and computational segmentation of surgical microanatomy. Further multidisciplinary research is required for its clinical applications and intraoperative integration.
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