NDLI: Learning-based mitotic cell detection in histopathological images

Content Provider	IEEE Xplore Digital Library
Author	Sommer, C. Fiaschi, L. Hamprecht, F.A. Gerlich, D.W.
Copyright Year	2012
Description	Author affiliation: Institute for Biochemistry, ETH Zürich (Sommer, C.; Gerlich, D.W.) \|\| HCI Heidelberg (Fiaschi, L.; Hamprecht, F.A.)
Abstract	Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From an initial training set a pixel-wise classifier is learned to segment candidate cells, which are then classified into mitotic and non-mitotic cells using object shape and texture features. Our workflow banks on two open source biomedical image analysis software: “ilastik” and “CellCognition” which provide a user user friendly interface to powerful learning algorithms, with the potential of making the pathologist work an easier task. We evaluate our approach on a dataset of 35 highresolution histopathological images from 5 different specimen (provided by International Conference for Pattern Recognition 2012 contest on Mitosis Detection in Breast Cancer Histological Images). Based on the candidate segmentation our approach achieves an area-under Precision-Recall-curve of 70% on an annotated dataset, with good localization accuracy, little parameter tuning and small user effort. Source code is provided.
Starting Page	2306
Ending Page	2309
File Size	1961551
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Image segmentation Breast cancer Training Accuracy Shape Pattern recognition Standards
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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