Image Processing

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

[+] Author Affiliations
Haibo Wang

Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States

Angel Cruz-Roa

Universidad Nacional de Colombia, Aulas de Ingenieria, MINDLab, 114 Edif. 453, Bogota, Colombia

Ajay Basavanhally

Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States

Hannah Gilmore

Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States

Natalie Shih

Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States

Mike Feldman

Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States

John Tomaszewski

University at Buffalo, School of Medicine and Biomedical Sciences, Buffalo, New York 14214, United States

Fabio Gonzalez

Universidad Nacional de Colombia, Aulas de Ingenieria, MINDLab, 114 Edif. 453, Bogota, Colombia

Anant Madabhushi

Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States

J. Med. Imag. 1(3), 034003 (Oct 10, 2014). doi:10.1117/1.JMI.1.3.034003
History: Received May 16, 2014; Revised September 14, 2014; Accepted September 16, 2014
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Abstract.  Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 HPFs (400× magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Our approach is accurate, fast, and requires fewer computing resources compared to existent methods, making this feasible for clinical use.

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Haibo Wang ; Angel Cruz-Roa ; Ajay Basavanhally ; Hannah Gilmore ; Natalie Shih, et al.
"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features", J. Med. Imag. 1(3), 034003 (Oct 10, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034003


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