Lesion detection for cardiac ablation from auto-fluorescence hyperspectral images

© 2018 SPIE. 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