Learning to Detect Cells Using Non-overlapping Extremal Regions

Arteta, Carlos; Lempitsky, Victor; Noble, J. Alison; Zisserman, Andrew

doi:10.1007/978-3-642-33415-3_43

Cited by 146 publications

(163 citation statements)

References 15 publications

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“…These methods may fail to detect spindle-like nuclei and irregular-shaped malignant epithelial nuclei. Arteta et al [17] employed maximally stable extremal regions for detection, which is likely to fall victim to weakly stained nuclei or nuclei with irregular chromatin texture. Ali et al [18] proposed an active contour-based approach to detect and segment overlapping nuclei based on shape models, which is highly variable in the case of tumor nuclei.…”

Section: Related Workmentioning

confidence: 99%

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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

1,103

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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Section: Related Workmentioning

confidence: 99%

“…Ensemble predictor (17), defined in this way, is essentially a weighted sum of all relevant predictors (Fig. 3b).…”

Section: B Neighboring Ensemble Predictor (Nep)mentioning

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

1,103

703

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“…The carcinogenic process that disrupts the ability of epithelia to connect with one another results in disturbed tubule formation with irregular 65 Gaussian mixture model and EM ACM Breast cancer 67 Single-path voting and mean shift Level set-based ACM Breast cancer 61 Watershed-based over-segmentation Hybrid ACM Her2-positive breast cancer 64 Region-growing and Markov random field H&E + fluorescence 56 Maximally stable extremal region detector (MSER) H&E + IHC 71 Color deconvolution Breast cancer 46 Adaptive thresholding and morphological operation Breast cancer 72 Convolutional neural network Breast cancer 41 Stacked autoencoder neural network Breast cancer 73 Convolutional autoencoder neural network EM: expectation maximization; ACM: active contour model.…”

Section: Image Preprocessingmentioning

confidence: 99%

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Chen

et al. 2017

Tumour Biol.

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With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

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“…2(c)]. Non-overlapping extremal region detection [24] is used to detect the centroids of all potential bright and dark lobes. In our approach, each connected component is iteratively eroded using a circular kernel of radius r er , leading to its split into smaller constituents, which are then iteratively processed.…”

Section: Image Processing Pipelinementioning

confidence: 99%

Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images

Bergeles¹,

Dubis

Davidson³

et al. 2017

Biomed. Opt. Express

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Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.

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Learning to Detect Cells Using Non-overlapping Extremal Regions

Cited by 146 publications

References 15 publications

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

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

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images

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