mlsanityData to 14 Oct 2026

Video-rate multispectral imaging in laparoscopic surgery: First-in-human application

arXiv 2105.13901 ↗First posted 28 May 2021cs.LG eess.IVcited by 6

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Abstract (arXiv)

Multispectral and hyperspectral imaging (MSI/HSI) can provide clinically relevant information on morphological and functional tissue properties. Application in the operating room (OR), however, has so far been limited by complex hardware setups and slow acquisition times. To overcome these limitations, we propose a novel imaging system for video-rate spectral imaging in the clinical workflow. The system integrates a small snapshot multispectral camera with a standard laparoscope and a clinically commonly used light source, enabling the recording of multispectral images with a spectral dimension of 16 at a frame rate of 25 Hz. An ongoing in patient study shows that multispectral recordings from this system can help detect perfusion changes in partial nephrectomy surgery, thus opening the doors to a wide range of clinical applications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 13 of its 84 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

13 of 84 references.

References · 13

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1 more
  1. 2019Out of distribution detection for intra-operative functional imagingcited by 1

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Modelscited by 19
  2. 2024OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotationscited by 2
  3. 2025CARL: Camera-Agnostic Representation Learning for Spectral Image Analysiscited by 2
  4. 2024Deep intra-operative illumination calibration of hyperspectral camerascited by 1
  5. 2024Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysiscited by 0
  6. 2025Clinical Validation of Deep Learning for Real-Time Tissue Oxygenation Estimation Using Spectral Imagingcited by 0