Automatic Nuclei Segmentation in H&amp;E Stained Breast Cancer Histopathology Images

Veta, Mitko; Diest, Paul J. van; Kornegoor, Robert; Huisman, André; Viergever, Max A.; Pluim, Josien P. W.

doi:10.1371/journal.pone.0070221

Cited by 320 publications

(222 citation statements)

References 29 publications

(35 reference statements)

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“…The MICCAI 2013 Grand Challenge on Mitosis Detection (Veta et al, 2013) also was won by an object-detecting GPU-MPCNN ensemble . Its data set was even larger and more challenging than the one of ICPR 2012 (Sec.…”

Section: -: More Contests and Benchmark Recordsmentioning

confidence: 99%

Deep learning in neural networks: An overview

2015

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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

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Section: -: More Contests and Benchmark Recordsmentioning

confidence: 99%

Deep learning in neural networks: An overview

2015

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“…Kuse et al [15] employed local phase symmetry to detect bilaterally symmetrical nuclei. Similarly, Veta et al [16] relied on the direction of gradient to identify the center of nuclei. These methods may fail to detect spindle-like nuclei and irregular-shaped malignant epithelial nuclei.…”

Section: Related Workmentioning

confidence: 99%

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana

Raza

Tsang

et al. 2016

IEEE Trans. Med. Imaging

1,048

703

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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract-Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer.

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“…This unique advantage allows the system to develop multiple color and pixel identifiers from digital images of HE stained pancreatic sections of uninjured and injured tissue. Similar technology has recently been used in several other pathology applications, including the identification of tumor cell nuclei in breast and prostate cancer [10,29,30] and apoptosis in cell lines [31].…”

Section: Discussionmentioning

confidence: 99%

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

et al. 2014

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The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%60.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%60.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.

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Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

Cited by 320 publications

References 29 publications

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

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