Manifold Embedding and Semantic Segmentation for Intraoperative Guidance with Hyperspectral Brain Imaging

Daniele Ravi, Himar Fabelo, Gustavo Marrero Callic, Guang Zhong Yang

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Original languageEnglish
Article number7907323
Pages (from-to)1845-1857
Number of pages13
JournalIEEE Transactions on Medical Imaging (T-MI)
Issue number9
Publication statusPublished - Sept 2017


  • brain cancer detection
  • hyperspectral imaging
  • Manifold embedding
  • semantic segmentation


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