Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

By Peter Naylor, Marick Lae, Fabien Reyal, Thomas Walter. IEEE Transactions on Medical Imaging, 2019

666 citations recorded by the source corpus.

The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new method to automatically segment nuclei from Haematoxylin and Eosin (H&E) stained histopathology data with fully convolutional networks. In particular, we address the problem of segmenting touching nuclei by formulating the segmentation problem as a regression task of the distance map. We demonstrate superior performance of this approach as compared to other approaches using Convolutional Neural Networks.

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

View Paper: https://www.semanticscholar.org/paper/5d1d45c89ec13f3b0a83078759bb1dfbac695102

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Citations in the corpus

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Naylor et al. [185] proposed a post-processing method based on distance map to handle the issue of touching objects.”
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (2019, background): “Nuclei Segmentation: Accurate nuclei segmentation is important in deﬁning abnormal cell features such as nuclear atypia, abundant tumor cellularity, and other features that would be indicative of cancer progression [39]–[42].”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, methodology): “This problem has previously been mitigated by contour prediction [31, 62, 63], concave point detection [64] and distance map regression [65].”
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “The DIST model by Naylor et al. [42] adds an additional decoder branch next to the segmentation branch to detect nuclei markers for a watershed postprocessing algorithm.”
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, methodology): “From left to right: Kumar [27]; CoNSeP; CPM-15; CPM-17 [30] and TNBC [31].”
Artificial intelligence for digital and computational pathology (2023, neutral): “Patches, nuclei, pixels, etc.”
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images (2020, methodology): “In particular, HoVer-Net [16], CIA-Net [46], Micro-Net [45] and DIST [17] have been purpose-built for TABLE II”
A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “More recently, nucleus segmentation problem has been formulated as a regression task to predict a distance map with respect to centroids or boundaries of nuclei using fully convolutional networks (FCNs) to achieve both segmentation and computational performance gains over previous deep learning base”