AutoStitcher

An automated program for efficient and robust reconstruction of digitized whole histological sections from tissue fragments

Gregory Penzias*, Andrew Janowczyk, Asha Singanamalli, Mirabela Rusu, Natalie Shih, Michael Feldman, Phillip D. Stricker, Warick Delprado, Sarita Tiwari, Maret Böhm, Anne Maree Haynes, Lee Ponsky, Satish Viswanath, Anant Madabhushi

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In applications involving large tissue specimens that have been sectioned into smaller tissue fragments, manual reconstruction of a "pseudo whole-mount" histological section (PWMHS) can facilitate (a) pathological disease annotation, and (b) image registration and correlation with radiological images. We have previously presented a program called HistoStitcher, which allows for more efficient manual reconstruction than general purpose image editing tools (such as Photoshop). However HistoStitcher is still manual and hence can be laborious and subjective, especially when doing large cohort studies. In this work we present AutoStitcher, a novel automated algorithm for reconstructing PWMHSs from digitized tissue fragments. AutoStitcher reconstructs ("stitches") a PWMHS from a set of 4 fragments by optimizing a novel cost function that is domain-inspired to ensure (i) alignment of similar tissue regions, and (ii) contiguity of the prostate boundary. The algorithm achieves computational efficiency by performing reconstruction in a multi-resolution hierarchy. Automated PWMHS reconstruction results (via AutoStitcher) were quantitatively and qualitatively compared to manual reconstructions obtained via HistoStitcher for 113 prostate pathology sections. Distances between corresponding fiducials placed on each of the automated and manual reconstruction results were between 2.7%-3.2%, reflecting their excellent visual similarity.

Original languageEnglish
Article number29906
Pages (from-to)1-13
Number of pages13
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 26 Jul 2016
Externally publishedYes

Bibliographical note

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