In this paper, we present a novel superpixel based Region of Interest (ROI) search and segmentation algorithm. The proposed superpixel generation method differs from pioneer works due to its combination of boundary update and coarse-to-fine refinement for superpixel clustering. The former maintains the accuracy of segmentation, meanwhile, avoids much of unnecessary revisit to the ‘non-boundary’ pixels. The latter reduces the complexity by faster localizing those boundary blocks. The paper introduces the novel superpixel algorithm [10] to the problem of ROI detection and segmentation along with a coarse-to-fine refinement scheme over a set of image of different magnification. Extensive experiments indicates that the proposed method gives better accuracy and efficiency than other superpixel-based methods for lung cancer cell images. Moreover, the block-wise coarse-to-fine scheme enables a quick search and segmentation of ROIs in whole slide images, while, other methods still cannot.