Mitosis detection in breast cancer histological images An ICPR 2012 contest

By Ludovic Roux, Daniel Racoceanu, N. Loménie, Maria S. Kulikova, Humayun Irshad, J. Klossa, F. Capron, C. Genestie, G. le Naour, Metin Nafi Gurcan. 2013

317 citations recorded by the source corpus.

Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Results : Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Conclusions : Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.

Subfields: Artificial Intelligence, Molecular Biology, Pulmonary and Respiratory Medicine

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “2012 finally saw the first DL system (an ensemble of GPUMPCNNs, Section 5.19) to win a contest on visual object detection (Ciresan, Giusti, Gambardella, & Schmidhuber, 2013) in large images of several million pixels (ICPR, 2012; Roux et al., 2013).”
My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 (2013, background): “2012 saw the ﬁrst Deep learning system (a GPU-MCMPCNN [18, 19], TL 1.12) to win a contest on visual object detection in large images (as opposed to mere recognition/classiﬁcation ): the ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images [2, 74, 16].”
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks (2013, result): “Performance results for our approach (DNN) compared with competing approaches [15].”
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, background): “…of the first uses of deep learning in histopathology was the work of Ciresan et al in the International Conference on Pattern Recognition (ICPR) challenge in 2012 [16], which focused on fully automated recognition of mitotic figures in hematoxylin and eosin (H&E)-stained breast cancer tissue [17].”
Breast Cancer Histopathology Image Analysis: A Review (2014, unknown): “histopathology images with annotated mitotic figures were made available [91], [92], which sparked further development of different mitosis detection approaches [93]–[102].”
Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge (2018, background): “4 Conference of Pattern Recognition (ICPR) 2012 (Roux et al., 2013).”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, unknown): “[7,29]”
Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014, unknown): “This resulted in the organization of the MITOS challenge (Roux et al., 2013) in 2012 on the same topic, which was well attended and helped advance the state of the art for this problem.”
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, methodology): “This method reported 51% F-score during ICPR 2012 Contest [96].”
Deep learning in robotics: a review of recent research (2017, background): “recognition [28], video recognition [29], small-image identification [30], detection in biomedical imaging [31-33], and many others.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “detection contest [312], [313]; AMIDAI13 dataset [314], [315]; UCSB Bio-Segmentation benchmark [316]; hand-segmented U2OS/3T3 cell image dataset [317]; and ISBI 2013 cell tracking”