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Proceedings Paper

Feature-enhancing zoom to facilitate Ki-67 hot spot detection
Author(s): Jesper Molin; Kavitha Shaga Devan; Karin Wårdell; Claes Lundström
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Paper Abstract

Image processing algorithms in pathology commonly include automated decision points such as classifications. While this enables efficient automation, there is also a risk that errors are induced. A different paradigm is to use image processing for enhancements without introducing explicit classifications. Such enhancements can help pathologists to increase efficiency without sacrificing accuracy. In our work, this paradigm has been applied to Ki-67 hot spot detection. Ki-67 scoring is a routine analysis to quantify the proliferation rate of tumor cells. Cell counting in the hot spot, the region of highest concentration of positive tumor cells, is a method increasingly used in clinical routine. An obstacle for this method is that while hot spot selection is a task suitable for low magnification, high magnification is needed to discern positive nuclei, thus the pathologist must perform many zooming operations. We propose to address this issue by an image processing method that increases the visibility of the positive nuclei at low magnification levels. This tool displays the modified version at low magnification, while gradually blending into the original image at high magnification. The tool was evaluated in a feasibility study with four pathologists targeting routine clinical use. In a task to compare hot spot concentrations, the average accuracy was 75±4.1% using the tool and 69±4.6% without it (n=4). Feedback on the system, gathered from an observer study, indicate that the pathologists found the tool useful and fitting in their existing diagnostic process. The pathologists judged the tool to be feasible for implementation in clinical routine.

Paper Details

Date Published: 20 March 2014
PDF: 10 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410W (20 March 2014); doi: 10.1117/12.2043512
Show Author Affiliations
Jesper Molin, Chalmers Univ. of Technology (Sweden)
Linköping Univ. (Sweden)
Sectra AB (Sweden)
Kavitha Shaga Devan, Linköping Univ. (Sweden)
Karin Wårdell, Linköping Univ. (Sweden)
Claes Lundström, Linköping Univ. (Sweden)
Sectra AB (Sweden)


Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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