#145: Interactive segmentation relabeling for classification of whole-slide histopathology imagery


A. Haridas, F. Bunyak, and K. Palaniappan

IEEE Int. Symposium on Computer-Based Medical Systems (CBMS), pgs. 84-87, 2015

visualization, big data, biomedical, histopathology

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Abstract

Collecting ground-truth or gold standard annotations from expert pathologists for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading is an expensive and time consuming process. Efficient visualization and annotation tools are needed to enable groundtruthing large whole-slide imagery. KOLAM is our scalable, cross-platform framework for interactive visualization of 2D, 2D+t and 3D imagery of high spatial, temporal and spectral resolution. In the current work KOLAM has been extended to support rapid interactive labelling and correction of automatic image classifier-based region labels of the tissue microenvironment by pathologists. Besides annotating regions-of-interest (ROIs), KOLAM enables extraction of the corresponding large polygonal image subregions for input into automatic segmentation algorithms, single-click region label reassignment and maintaining hierarchical image subregions. Experience indicates that clinicians prefer simple-to-use interfaces that support rapid labelling of large image regions with minimal effort. The incorporation of easy-to-use tissue annotation features in KOLAM makes it an attractive candidate for integration within a multi-stage histopathology image analysis pipeline supporting assisted segmentation and labelling to improve whole-slide imagery (WSI) analytics.