---
title: MoNuSeg
canonical_url: "https://www.modelscope.cn/datasets/OmniData/MoNuSeg"
md_url: "https://www.modelscope.cn/datasets/OmniData/MoNuSeg.md"
repository: OmniData/MoNuSeg
last_updated: 2024-07-03
license: "[CC BY-NC-SA 4.0]"
storage_size: "183 MB"
domain:
  - publishDate
  - publisher
  - publishUrl
  - paperUrl
  - displayName
tasks:
  - 2018
  - "Chinese University of Hong Kong"
  - "https://monuseg.grand-challenge.org/Data/"
  - "https://www.dropbox.com/s/j3154xgkkpkri9w/IEEE_TMI_NuceliSegmentation.pdf?dl=0"
  - MoNuSeg
downloads: 792
stars: 0
---

# MoNuSeg

> MoNuSeg - OmniData 在 ModelScope 开源的数据集。displayName: MoNuSeg license: CC BY-NC-SA 4.0 paperUrl: https://www.dropbox.com/s/j3154xgkkpkri9w/IEEETMINuceliSegmentation.pdf?dl=0 publishDate: "2018" publishUrl: https://monuseg.grand-challenge.org/Data/ publisher:…

OmniData/MoNuSeg 是 ModelScope 魔搭社区上的2018、Chinese University of Hong Kong、https://monuseg.grand-challenge.org/Data/数据集，涉及 publishDate、publisher、publishUrl 领域，存储大小 183 MB，采用 [CC BY-NC-SA 4.0] 许可。

- **Repository**: OmniData/MoNuSeg
- **License**: [CC BY-NC-SA 4.0]
- **Tasks**: 2018, Chinese University of Hong Kong, https://monuseg.grand-challenge.org/Data/, https://www.dropbox.com/s/j3154xgkkpkri9w/IEEE_TMI_NuceliSegmentation.pdf?dl=0, MoNuSeg
- **Domain**: publishDate, publisher, publishUrl, paperUrl, displayName
- **Storage size**: 183 MB
- **Downloads**: 792
- **Stars**: 0
- **Last updated**: 2024-07-03

Source: https://www.modelscope.cn/datasets/OmniData/MoNuSeg

---

displayName: MoNuSeg
license:
- CC BY-NC-SA 4.0
paperUrl: https://www.dropbox.com/s/j3154xgkkpkri9w/IEEE_TMI_NuceliSegmentation.pdf?dl=0
publishDate: "2018"
publishUrl: https://monuseg.grand-challenge.org/Data/
publisher:
- Case Western Reserve University
- Indian Institute of Technology Mumbai
- University of Alberta
- Chinese University of Hong Kong
tags:
- Tumour
- Nuclei Segmentation

---
# 数据集介绍
  ## 简介
  此挑战的数据集是通过仔细注释几名患有不同器官肿瘤且在多家医院被诊断的患者的组织图像而获得的。该数据集是通过从TCGA档案中下载以40倍放大倍率捕获的H & E染色组织图像创建的。H & E染色是增强组织切片对比度的常规方案，通常用于肿瘤评估 (分级，分期等)。鉴于跨多个器官和患者的细胞核外观的多样性，以及在多家医院采用的染色方案的丰富性，培训数据集将使能够开发强大且可推广的细胞核分割技术，这些技术将开箱即用。
  
## Download dataset
:modelscope-code[]{type="git"}