Literature DB >> 24831181

Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology.

H Irshad1, A Gouaillard2, L Roux3, D Racoceanu4.   

Abstract

Breast cancer is the second most frequent cancer. The reference process for breast cancer prognosis is Nottingham grading system. According to this system, mitosis detection is one of the three important criteria required for grading process and quantifying the locality and prognosis of a tumor. Multispectral imaging, as relatively new to the field of histopathology, has the advantage, over traditional RGB imaging, to capture spectrally resolved information at specific frequencies, across the electromagnetic spectrum. This study aims at evaluating the accuracy of mitosis detection on histopathological multispectral images. The proposed framework includes: selection of spectral bands and focal planes, detection of candidate mitotic regions and computation of morphological and multispectral statistical features. A state-of-the-art of the methods for mitosis classification is also provided. This framework has been evaluated on MITOS multispectral dataset and achieved higher detection rate (67.35%) and F-Measure (63.74%) than the best MITOS contest results (Roux et al., 2013). Our results indicate that the selected multispectral bands have more discriminant information than a single spectral band or all spectral bands for mitotic figures, validating the interest of using multispectral images to improve the quality of the diagnostic in histopathology.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Breast cancer; Classification; Features extraction; Histopathology; Multispectral images; Object detection; Spectral bands selection; Texture characterization

Mesh:

Year:  2014        PMID: 24831181     DOI: 10.1016/j.compmedimag.2014.04.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  9 in total

1.  Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images.

Authors:  Samuel Ortega; Martin Halicek; Himar Fabelo; Raul Guerra; Carlos Lopez; Marylene Lejaune; Fred Godtliebsen; Gustavo M Callico; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-16

2.  Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

Authors:  Ramin Nateghi; Habibollah Danyali; Mohammad Sadegh Helfroush
Journal:  J Med Syst       Date:  2017-08-14       Impact factor: 4.460

Review 3.  Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods.

Authors:  Dorra Nouri; Yves Lucas; Sylvie Treuillet
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-07-04       Impact factor: 2.924

4.  A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images Using Deep Belief Networks.

Authors:  K Sabeena Beevi; Madhu S Nair; G R Bindu
Journal:  IEEE J Transl Eng Health Med       Date:  2017-04-25       Impact factor: 3.316

5.  A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis.

Authors:  Wenlou Liu; Linwei Wang; Jiuyang Liu; Jingping Yuan; Jiamei Chen; Han Wu; Qingming Xiang; Guifang Yang; Yan Li
Journal:  Transl Oncol       Date:  2016-11-08       Impact factor: 4.243

6.  Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

Authors:  Ruqayya Awan; Somaya Al-Maadeed; Rafif Al-Saady
Journal:  PLoS One       Date:  2018-06-06       Impact factor: 3.240

7.  Screening of the prognostic targets for breast cancer based co-expression modules analysis.

Authors:  Huijuan Liu; Hui Ye
Journal:  Mol Med Rep       Date:  2017-07-21       Impact factor: 2.952

8.  MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images.

Authors:  Salar Razavi; Fariba D Khameneh; Hana Nouri; Dimitrios Androutsos; Susan J Done; April Khademi
Journal:  J Pathol Inform       Date:  2022-01-20

9.  Computational pathology to discriminate benign from malignant intraductal proliferations of the breast.

Authors:  Fei Dong; Humayun Irshad; Eun-Yeong Oh; Melinda F Lerwill; Elena F Brachtel; Nicholas C Jones; Nicholas W Knoblauch; Laleh Montaser-Kouhsari; Nicole B Johnson; Luigi K F Rao; Beverly Faulkner-Jones; David C Wilbur; Stuart J Schnitt; Andrew H Beck
Journal:  PLoS One       Date:  2014-12-09       Impact factor: 3.240

  9 in total

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