Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging
Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging
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DOI：
10.1109/tmi.2017.2695523
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发表时间：
2017-09-01
影响因子：
10.6
通讯作者：
Yang, Guang-Zhong
中科院分区：
文献类型：
--
作者：
Ravi, Daniele;Fabelo, Himar;Yang, Guang-Zhong
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 intraoperative 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.
