Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

By Humayun Irshad, Antoine Veillard, Ludovic Roux, Daniel Racoceanu. IEEE Reviews in Biomedical Engineering, 2014

645 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/13de33ee941f1ebf3ed185c20fb4453a07302c30

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Citations in the corpus

Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, neutral): “(Irshad et al., 2014) has compiled a resource in covering the extensive collection of nuclear segmentation papers for histologic analysis over the past 20 years.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, neutral): “A comprehensive review of these initiatives appears in [160]–[162].”
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, methodology): “An alternative and more efficient approach which is taken in this paper is to use morphological clues in local neighborhoods to develop automated cellular recognition via image analysis which can then be deployed for sophisticated tissue morphometry in future [6].”
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015, neutral): “Most current nuclei detection/segmentation methods on H&E stained images are based on exploiting low-level hand-crafted features [6], such as color [7]–[22], edge [23]–[28], contextual information [29]–[31], and texture [32], [33]; see Table I for a detailed enumeration of some of these approaches.”
A Multi-Organ Nucleus Segmentation Challenge (2020, background): “More comprehensive reviews of state-of-the-art nucleus segmentation algorithms can be found in [26] and [27].”
DCAN: Deep contour‐aware networks for object instance segmentation from histology images (2017, methodology): “…of applications, such as gland segmentation for colon cancer diagnosis ( Sirinukunwattana et al., 2015a ), nuclei segmentation for cancer grading ( Irshad et al., 2014 ), and cell segmentation and tracking for understanding cellular mechanisms ( Meijering, 2012 ), etc. Previous methods such as…”
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map (2019, background): “has been addressed by many authors with a variety of traditional approaches, including mathematical morphology [13], [19]–[21], pixel classification [22], level sets [23] and graph-based segmentation methods [24] (see also [25], [26] for extensive reviews).”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, background): “[40,41] A recent review of nuclei segmentation literature [42] shows that detecting these nuclei tends not to be extremely challenging, but accurately finding their borders and/or dividing overlapping nuclei is the current challenge.”
Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images (2018, background): “Review of glass tissue slides under a high-power microscope by a pathologist is the de facto standard for cancer diagnosis.(1) This manual process is labor intensive and time consuming(2), and is not scalable to large image datasets, which are desirable in research studies.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[21] have reported a survey on the methods for nucleus detection, segmentation, feature extraction, and classification on hematoxylin and eosin (H&E)”
Deep Learning for Classification of Colorectal Polyps on Whole-slide Images (2017, background): “Furthermore, we can see that differentiation between hyperplastic polyps and sessile serrated polyp is another major source of mistakes for our model, which is aligned with gastrointestinal pathologists’ experience in this task [45, 16, 44].”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “Nowadays, the current challenge is to precisely define cell boundaries or/and divide overlapping nuclei [179].”
Understanding Deep Learning Techniques for Image Segmentation (2019, background): “Microscopic images [85] also need various kinds of segmentations like cell or nuclei detection, counting number of cells, cell structure analysis for cancer detection and so on.”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, methodology): “…stain is routine for trained clinicians and researchers, chromatic stain variability, nuclear overlap and occlusion, variability in optical image quality, and differences in nuclear and cytoplasmic morphology and stain density provide challenges for computer-based segmentation algorithms [22– 24].”