Context-sensitive patch histograms for detecting rare events in histopathological data

Kristians Diaz; Maximilian Baust; Nassir Navab

doi:10.1117/12.2254014

1 March 2017 Context-sensitive patch histograms for detecting rare events in histopathological data

Kristians Diaz, Maximilian Baust, Nassir Navab

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400F (2017) https://doi.org/10.1117/12.2254014
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Assessment of histopathological data is not only difficult due to its varying appearance, e.g. caused by staining artifacts, but also due to its sheer size: Common whole slice images feature a resolution of 6000x4000 pixels. Therefore, finding rare events in such data sets is a challenging and tedious task and developing sophisticated computerized tools is not easy, especially when no or little training data is available. In this work, we propose learning-free yet effective approach based on context sensitive patch-histograms in order to find extramedullary hematopoiesis events in Hematoxylin-Eosin-stained images. When combined with a simple nucleus detector, one can achieve performance levels in terms of sensitivity 0.7146, specificity 0.8476 and accuracy 0.8353 which are very well comparable to a recently published approach based on random forests.

Conference Presentation

Citation Download Citation

Kristians Diaz, Maximilian Baust, and Nassir Navab "Context-sensitive patch histograms for detecting rare events in histopathological data", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400F (1 March 2017); https://doi.org/10.1117/12.2254014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available