Nerve detection and visualization using hyperspectral imaging for surgical guidance Article Swipe
Minh Tran
,
Michelle D. Bryarly
,
Ling Ma
,
Muhammad Saad Yousuf
,
Theodore J. Price
,
Baowei Fei
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/12.3008470
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/12.3008470
During surgery of delicate regions, differentiation between nerve and surrounding tissue is crucial. Hyperspectral imaging (HSI) techniques can enhance the contrast between types of tissue beyond what the human eye can differentiate. Whereas an RGB image captures 3 bands within the visible light range (e.g., 400 nm to 700 nm), HSI can acquire many bands in wavelength increments that highlight regions of an image across a wavelength spectrum. We developed a workflow to identify nerve tissues from other similar tissues such as fat, bone, and muscle. Our workflow uses spectral angle mapper (SAM) and endmember selection. The method is robust for different types of environment and lighting conditions. We validated our workflow on two samples of human tissues. We used a compact HSI system that can image from 400 to 1700 nm to produce HSI of the samples. On these two samples, we achieved an intersection-over-union (IoU) segmentation score of 84.15% and 76.73%, respectively. We showed that our workflow identifies nerve segments that are not easily seen in RGB images. This method is fast, does not rely on special hardware, and can be applied in real time. The hyperspectral imaging and nerve detection approach may provide a powerful tool for image-guided surgery.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.3008470
- OA Status
- green
- Cited By
- 2
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1117/12.3008470Digital Object Identifier
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Nerve detection and visualization using hyperspectral imaging for surgical guidanceWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-02-16Full publication date if available
- Authors
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Minh Tran, Michelle D. Bryarly, Ling Ma, Muhammad Saad Yousuf, Theodore J. Price, Baowei FeiList of authors in order
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https://doi.org/10.1117/12.3008470Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/11070131Direct OA link when available
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Hyperspectral imaging, Visualization, Computer science, Medical imaging, Computer vision, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.nm), | 49 |
| abstract_inverted_index.real | 185 |
| abstract_inverted_index.rely | 176 |
| abstract_inverted_index.seen | 166 |
| abstract_inverted_index.such | 80 |
| abstract_inverted_index.that | 58, 124, 156, 162 |
| abstract_inverted_index.tool | 198 |
| abstract_inverted_index.used | 119 |
| abstract_inverted_index.uses | 88 |
| abstract_inverted_index.what | 26 |
| abstract_inverted_index.(HSI) | 15 |
| abstract_inverted_index.(IoU) | 146 |
| abstract_inverted_index.(SAM) | 92 |
| abstract_inverted_index.angle | 90 |
| abstract_inverted_index.bands | 38, 54 |
| abstract_inverted_index.bone, | 83 |
| abstract_inverted_index.fast, | 173 |
| abstract_inverted_index.human | 28, 116 |
| abstract_inverted_index.image | 35, 63, 126 |
| abstract_inverted_index.light | 42 |
| abstract_inverted_index.nerve | 7, 74, 160, 191 |
| abstract_inverted_index.other | 77 |
| abstract_inverted_index.range | 43 |
| abstract_inverted_index.score | 148 |
| abstract_inverted_index.these | 139 |
| abstract_inverted_index.time. | 186 |
| abstract_inverted_index.types | 22, 102 |
| abstract_inverted_index.84.15% | 150 |
| abstract_inverted_index.During | 0 |
| abstract_inverted_index.across | 64 |
| abstract_inverted_index.beyond | 25 |
| abstract_inverted_index.easily | 165 |
| abstract_inverted_index.mapper | 91 |
| abstract_inverted_index.method | 97, 171 |
| abstract_inverted_index.robust | 99 |
| abstract_inverted_index.showed | 155 |
| abstract_inverted_index.system | 123 |
| abstract_inverted_index.tissue | 10, 24 |
| abstract_inverted_index.within | 39 |
| abstract_inverted_index.76.73%, | 152 |
| abstract_inverted_index.Whereas | 32 |
| abstract_inverted_index.acquire | 52 |
| abstract_inverted_index.applied | 183 |
| abstract_inverted_index.between | 6, 21 |
| abstract_inverted_index.compact | 121 |
| abstract_inverted_index.enhance | 18 |
| abstract_inverted_index.images. | 169 |
| abstract_inverted_index.imaging | 14, 189 |
| abstract_inverted_index.muscle. | 85 |
| abstract_inverted_index.produce | 133 |
| abstract_inverted_index.provide | 195 |
| abstract_inverted_index.regions | 60 |
| abstract_inverted_index.samples | 114 |
| abstract_inverted_index.similar | 78 |
| abstract_inverted_index.special | 178 |
| abstract_inverted_index.surgery | 1 |
| abstract_inverted_index.tissues | 75, 79 |
| abstract_inverted_index.visible | 41 |
| abstract_inverted_index.achieved | 143 |
| abstract_inverted_index.approach | 193 |
| abstract_inverted_index.captures | 36 |
| abstract_inverted_index.contrast | 20 |
| abstract_inverted_index.crucial. | 12 |
| abstract_inverted_index.delicate | 3 |
| abstract_inverted_index.identify | 73 |
| abstract_inverted_index.lighting | 106 |
| abstract_inverted_index.powerful | 197 |
| abstract_inverted_index.regions, | 4 |
| abstract_inverted_index.samples, | 141 |
| abstract_inverted_index.samples. | 137 |
| abstract_inverted_index.segments | 161 |
| abstract_inverted_index.spectral | 89 |
| abstract_inverted_index.surgery. | 201 |
| abstract_inverted_index.tissues. | 117 |
| abstract_inverted_index.workflow | 71, 87, 111, 158 |
| abstract_inverted_index.detection | 192 |
| abstract_inverted_index.developed | 69 |
| abstract_inverted_index.different | 101 |
| abstract_inverted_index.endmember | 94 |
| abstract_inverted_index.hardware, | 179 |
| abstract_inverted_index.highlight | 59 |
| abstract_inverted_index.spectrum. | 67 |
| abstract_inverted_index.validated | 109 |
| abstract_inverted_index.identifies | 159 |
| abstract_inverted_index.increments | 57 |
| abstract_inverted_index.selection. | 95 |
| abstract_inverted_index.techniques | 16 |
| abstract_inverted_index.wavelength | 56, 66 |
| abstract_inverted_index.conditions. | 107 |
| abstract_inverted_index.environment | 104 |
| abstract_inverted_index.surrounding | 9 |
| abstract_inverted_index.image-guided | 200 |
| abstract_inverted_index.segmentation | 147 |
| abstract_inverted_index.(<i>e.g.,</i> | 44 |
| abstract_inverted_index.Hyperspectral | 13 |
| abstract_inverted_index.hyperspectral | 188 |
| abstract_inverted_index.respectively. | 153 |
| abstract_inverted_index.differentiate. | 31 |
| abstract_inverted_index.differentiation | 5 |
| abstract_inverted_index.intersection-over-union | 145 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.74951815 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |