A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

By Neeraj Kumar, Ruchika Verma, Sanuj Sharma, S. Bhargava, Abhishek Vahadane, A. Sethi. IEEE Transactions on Medical Imaging, 2017

1,098 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/34c062e2b8a3f6421b9f4ff22f115a36d4aba823

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “The MoNuSeg[66, 67] dataset serves as an additional dataset for nuclei segmentation.”
Artificial intelligence for digital and computational pathology (2023, neutral): “Patches, nuclei, pixels, etc.”
Machine Learning Methods for Histopathological Image Analysis (2017, background): “[99] Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A.”
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images (2020, background): “[16]–[18]; mitosis detection [19]; cancer type prediction [20] and cancer grading [21], [22].”
Transformers in Medical Imaging: A Survey (2022, methodology): “Extensive experimentation on brain anatomy segmentation [160], gland segmentation [161], and MoNuSeg (microscopy) [162] demonstrate the effectiveness of their proposed gated axial attention module.”
SegDiff: Image Segmentation with Diffusion Probabilistic Models (2021, methodology): “The MoNuSeg dataset [25, 26] contains a training set with 30 microscopic images from seven organs, with annotations of 21,623 individual nuclei.”
Deep learning for cellular image analysis (2019, methodology): “Excitingly, deep learning recently was used for instance segmentation in pathology images 99,105,106 , positioning Keren et al. 11 to quantify interactions between tumor cells and immune cells in spatial-proteomics measurements of”
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “Automated image analysis tools enable pathologists to scrutinise tissues microscopically, identifying subtle histopathological attributes often overlooked by the human eye [46].”
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (2019, positive): “Previous works rely on conventional fully convolutional networks that minimize a pixel-wise loss [43], which can cause the network to segment multiple nuclei as one, leading to inaccurate feature extraction of nuclei shape and community structure.”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “The UCTransNet network performs SOTA Dice results on the GlaS [148], MoNuSeg [149], [150] and Synapse [89] datasets in comparison with TransUNet [38].”
A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “detection and segmentation tasks by significant margins [1], [27]–[30].”
Digital Pathology and Artificial Intelligence (2019, background): “In [17], the authors created a well annotated database consisting of 30 WSI of digitized tissue samples from several organs.”
Regression Concept Vectors for Bidirectional Explanations in Histopathology (2018, methodology): “To extract concepts, we used the nuclei segmentation data set in [8], for which no labels of tumorous and non-tumorous regions were available.”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “To solve this problem, several algorithms based on deep learning have been proposed to obtain an accurate segmentation of nuclei in histopathological images [1,180].”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, methodology): “completed seminal work in multi-organ nuclei segmentation by posing the challenge as a three class problem (CNN-3C)[31].”