Dr. Narine Muselimyan Profile

Dr. Narine Muselimyan

at George Washington Univ

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Proceedings Article | 12 March 2018 Paper

Lesion detection for cardiac ablation from auto-fluorescence hyperspectral images

Shuyue Guan, Murray Loew, Huda Asfour, Narine Sarvazyan, Narine Muselimyan

Proceedings Volume 10578, 105781O (2018) https://doi.org/10.1117/12.2293652

KEYWORDS: Tissues, Radiofrequency ablation, Visualization, Hyperspectral imaging, Atrial fibrillation, Machine learning, Binary data, Image processing, Cameras, Ultraviolet radiation

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Direct visualization of the ablated region in the left atrium during radiofrequency ablation (RFA) surgery for treating atrial fibrillation (AF) can improve therapy success rates. Our visualization approach is auto-fluorescence hyperspectral imaging (aHSI), which constructs each hypercube containing 31 auto-fluorescence images of the tissue. We wish to use the spectral information to characterize ablated lesions as being successful or not. In this paper, we reshaped one hypercube to a 2D matrix. Each row (sample) in the matrix represents a pixel in the spatial dimension, and the matrix has 31 columns corresponding to 31 spectral features. Then, we applied k-means clustering to detect ablated regions without a priori knowledge about the lesion. We introduced an accuracy index to evaluate the results of k-means by comparing with the reference truth images quantitatively. To speed-up the detection process, we implemented a grouping procedure to decrease the number of features. The 31 features were divided into four contiguous disjoint groups. In each group, the summation of values yielded a new feature. By the same evaluation method, we found the best 4-feature groups to adequately detect the lesions from all possible combinations. The average accuracy for detection by k-means (k=10) using 31 features was about 74% of reference truth images. And, for using the best grouped 4 features, it was about 95% of that using 31 features. The time cost of 4-feature clustering is about 41% of the 31-feature clustering. We expect that the reduction of time for both acquisition and processing will make possible intraoperative real-time display of ablation status.

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