# A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images

arXiv: https://arxiv.org/abs/1803.02786 · HTML: https://mlsanity.com/paper/1803.02786/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation](https://mlsanity.com/paper/2206.15134/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Realistic Data Enrichment for Robust Image Segmentation in Histopathology](https://mlsanity.com/paper/2304.09534/) (2023)
- [Harnessing Artificial Intelligence to Infer Novel Spatial Biomarkers for the Diagnosis of Eosinophilic Esophagitis](https://mlsanity.com/paper/2205.13583/) (2022)
- [PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics](https://mlsanity.com/paper/2103.02015/) (2021)
- [Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology](https://mlsanity.com/paper/2302.06549/) (2023)
- [Financial Table Extraction in Image Documents](https://mlsanity.com/paper/2405.05260/) (2024)
- [A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing](https://mlsanity.com/paper/2202.08682/) (2022)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section](https://mlsanity.com/paper/2111.05789/) (2021)
- [PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation](https://mlsanity.com/paper/2411.16515/) (2024)
- [Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation](https://mlsanity.com/paper/2209.00585/) (2022)

## Abstract (arXiv)

This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an auto- matic end-to-end deep neural network algorithm for segmenta- tion 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 1000X1000 image can be segmented in less than 5 seconds. This makes it possible to precisely segment the whole-slide image in acceptable time