Assessment of algorithms for mitosis detection in breast cancer histopathology images

Autor: F. Boray Tek, Stefan M. Willems, Bogdan J. Matuszewski, Josien P. W. Pluim, Satoshi Kondo, Paul J. van Diest, Anders Boesen Lindbo Larsen, Fabio A. González, Violet Snell, Evdokia Arkoumani, Josef Kittler, Alessandro Giusti, Jacob S. Vestergaard, Adnan Mujahid Khan, Thomas Walter, Luca Maria Gambardella, Mitko Veta, Ching-Wei Wang, Frédéric Precioso, Anant Madabhushi, Max A. Viergever, Teofilo de Campos, Miangela M. Lacle, Anders Bjorholm Dahl, Haibo Wang, Angel Cruz-Roa, Dan Ciresan, Nasir M. Rajpoot, Jürgen Schmidhuber
Přispěvatelé: Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Işık University, Faculty of Engineering, Department of Computer Engineering, Tek, Faik Boray, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Medical Image Analysis
Jazyk: angličtina
Rok vydání: 2015
Předmět:
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
H&E stain
SDG 3 – Goede gezondheid en welzijn
0302 clinical medicine
Breast cancer
Whole slide imaging
Non-U.S. Gov't
ComputingMilieux_MISCELLANEOUS
Observer Variation
0303 health sciences
Radiological and Ultrasound Technology
Research Support
Non-U.S. Gov't
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Computer Graphics and Computer-Aided Design
3. Good health
Radiology Nuclear Medicine and imaging
030220 oncology & carcinogenesis
Female
Computer Vision and Pattern Recognition
Algorithm
Algorithms
medicine.medical_specialty
FEASIBILITY
COUNTING MITOSES
Mitosis
Breast Neoplasms
Health Informatics
Research Support
Cancer grading
03 medical and health sciences
SDG 3 - Good Health and Well-being
Medical imaging
medicine
Journal Article
Humans
Digital pathology
Radiology
Nuclear Medicine and imaging
Comparative Study
Grading (tumors)
030304 developmental biology
SECTIONS
business.industry
medicine.disease
Mitotic Figure
Histopathology
business
Mitosis detection
Zdroj: Medical Image Analysis
Medical Image Analysis, Elsevier, 2015, 20 (1), pp.237-248. ⟨10.1016/j.media.2014.11.010⟩
Medical image analysis
Medical Image Analysis, 20(1), 237. Elsevier
Veta, M, van Diest, P J, Willems, S M, Wang, H, Madabhushi, A, Cruz-Roa, A, Gonzalez, F, Larsen, A B L, Vestergaard, J S, Dahl, A B, Ciresan, D C, Schmidhuber, J, Giusti, A, Gambardella, L M, Tek, F B, Walter, T, Wang, C-W, Kondo, S, Matuszewski, B J, Precioso, F, Snell, V, Kittler, J, de Campos, T E, Khan, A M, Rajpoot, N M, Arkoumani, E, Lacle, M M, Viergever, M A & Pluim, J P W 2015, ' Assessment of algorithms for mitosis detection in breast cancer histopathology images ', Medical Image Analysis, vol. 20, no. 1, pp. 237-248 . https://doi.org/10.1016/j.media.2014.11.010
Medical Image Analysis, 20(1), 237-248. Elsevier
ISSN: 1361-8415
1361-8423
DOI: 10.1016/j.media.2014.11.010⟩
Popis: The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.
Comment: 23 pages, 5 figures, accepted for publication in the journal Medical Image Analysis
Databáze: OpenAIRE
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