ArticleMedical & biological engineering & computing2019
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Scalable Nuclei Detection in HER2-SISH Whole Slide Images via Fine-Tuned Stardist with Expert-Annotated Regions of Interest.Diagnostics (Basel, Switzerland) · 2025Article
- iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.Genome biology · 2024Article
- Automated segmentation and recognition of C. elegans whole-body cells.Bioinformatics (Oxford, England) · 2024Article
- Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features.Bioengineering (Basel, Switzerland) · 2024Article
- HistoMIL: A Python package for training multiple instance learning models on histopathology slides.iScience · 2023Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images.Biomimetics (Basel, Switzerland) · 2023Article
- DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation.PloS one · 2023Article
- Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network.Diagnostics (Basel, Switzerland) · 2022Article
- A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.Journal of applied crystallography · 2022Article
- Singular Nuclei Segmentation for AutomaticSensors (Basel, Switzerland) · 2022Article
- A Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN).Diagnostic pathology · 2022Article
- Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis.Frontiers in medicine · 2022Article
- A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.Frontiers in medicine · 2022Article
- Article
- Automatic Segmentation of Bone Canals in Histological Images.Journal of digital imaging · 2021Article
- System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL).Scientific reports · 2021Article
- Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.Sensors (Basel, Switzerland) · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation . Graphical Abstract The neural network for nuclei segmentation.
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