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Peter, L.* ; Mateus, D. ; Chatelain, P.* ; Schworm, N.* ; Stangl, S.* ; Multhoff, G. ; Navab, N.*

Leveraging random forests for interactive exploration of large histological images.

Lecture Notes Comp. Sci. 8673, 1-8 (2015)
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Open Access Green
The large size of histological images combined with their very challenging appearance are two main difficulties which considerably complicate their analysis. In this paper, we introduce an interactive strategy leveraging the output of a supervised random forest classifier to guide a user through such large visual data. Starting from a forest-based pixelwise estimate, subregions of the images at hand are automatically ranked and sequentially displayed according to their expected interest. After each region suggestion, the user selects among several options a rough estimate of the true amount of foreground pixels in this region. From these one-click inputs, the region scoring function is updated in real time using an online gradient descent procedure, which corrects on-the-fly the shortcomings of the initial model and adapts future suggestions accordingly. Experimental validation is conducted for extramedullary hematopoesis localization and demonstrates the practical feasibility of the procedure as well as the benefit of the online adaptation strategy.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Konferzenzdatum September 14-18, 2014
Konferenzort Boston, MA, USA
Konferenzband Proceedings, Part I
Quellenangaben Band: 8673, Heft: , Seiten: 1-8 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Begutachtungsstatus