Automated Reconstruction Algorithm for Identification of 3D Architectures of Cribriform Ductal Carcinoma In Situ

Kerri-Ann Norton, Sameera Namazi, Nicola Barnard, Mariko Fujibayashi, Gyan Bhanot, Shridar Ganesan, Hitoshi Iyatomi, Koichi Ogawa and Troy Shinbrot

PLOS ONE, 2012, vol. 7, issue 9, 1-11

Abstract: Ductal carcinoma in situ (DCIS) is a pre-invasive carcinoma of the breast that exhibits several distinct morphologies but the link between morphology and patient outcome is not clear. We hypothesize that different mechanisms of growth may still result in similar 2D morphologies, which may look different in 3D. To elucidate the connection between growth and 3D morphology, we reconstruct the 3D architecture of cribriform DCIS from resected patient material. We produce a fully automated algorithm that aligns, segments, and reconstructs 3D architectures from microscopy images of 2D serial sections from human specimens. The alignment algorithm is based on normalized cross correlation, the segmentation algorithm uses histogram equilization, Otsu's thresholding, and morphology techniques to segment the duct and cribra. The reconstruction method combines these images in 3D. We show that two distinct 3D architectures are indeed found in samples whose 2D histological sections are similarly identified as cribriform DCIS. These differences in architecture support the hypothesis that luminal spaces may form due to different mechanisms, either isolated cell death or merging fronds, leading to the different architectures. We find that out of 15 samples, 6 were found to have ‘bubble-like’ cribra, 6 were found to have ‘tube-like’ criba and 3 were ‘unknown.’ We propose that the 3D architectures found, ‘bubbles’ and ‘tubes’, account for some of the heterogeneity of the disease and may be prognostic indicators of different patient outcomes.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0044011

DOI: 10.1371/journal.pone.0044011

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