Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons

Brian Falkenstein, Adriana Kovashka, Seong Jae Hwang, S Chakra Chennubhotla

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Abstract: In this study, we demonstrate the efficacy of scoring statistics derived from a medial axis transform, for differentiating tumor and non-tumor nuclei, in malignant breast tumor histopathology images. Characterizing nuclei shape is a crucial part of diagnosing breast tumors for human doctors, and these scoring metrics may be integrated into machine perception algorithms which aggregate nuclei information across a region to label whole breast lesions. In particular, we present a low-dimensional representation capturing characteristics of a skeleton extracted from nuclei. We show that this representation outperforms both prior morphological features, as well as CNN features, for classification of tumors. Nuclei and region scoring algorithms such as the one presented here can aid pathologists in the diagnosis of breast tumors.