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

By M. Veta, P. V. van Diest, R. Kornegoor, A. Huisman, Max A. Viergever, Josien P. W. Pluim. PLoS ONE, 2013

395 citations recorded by the source corpus.

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e312652daf82ed144d1696aae7ab412030d4f7eb

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Citations in the corpus

A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “Prior to the advent of deep learning, approaches to segment nuclei relied on watershed segmentation, morphological operations – such as erosion, dilation, opening and closing – color-based thresholding, and variants of active contours [12], [13], [21]–[23].”
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images (2018, methodology): “If the seed of a nucleus is generated, its contour could be obtained by using marker controlled watershed[9, 16] or region growing[17].”
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, background): “Similarly, Veta et al. [16] relied on the direction of gradient to identify the centers of nuclei.”
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019, background): “Previouswork on digital pathologyhasusedcomputer-basedimageanalysisapproachesfor celldetectionandclassification [6], tissueclassification[7], nucleiandmitosisdetection[8,9],microvessel segmentation [10], andother immunohistochemistry scoringtasks[11] in histopathological images.”
Breast Cancer Histopathology Image Analysis: A Review (2014, neutral): “the object [57], [58] have been used for both identification of nucleus markers and separation of segmented clumps.”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, methodology): “This post processing technique is similar to [27], [73].”