Hierarchical conditional random field model for multi‐object segmentation in gastric histopathology images
| Autor: | Changhao Sun, Jinghua Zhang, Xiaoyan Li, Chen Li, Frank Kulwa |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Conditional random field
Contextual image classification Pixel Computer science business.industry Pattern recognition Image segmentation Object (computer science) Convolutional neural network Graph (abstract data type) Segmentation Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Electronics Letters. 56:750-753 |
| ISSN: | 1350-911X 0013-5194 |
| Popis: | In this Letter, a hierarchical conditional random field (HCRF) model-based gastric histopathology image segmentation (GHIS) method is proposed, which can localise abnormal (cancer) regions in gastric histopathology images to assist histopathologists in medical work. First, to obtain pixel-level segmentation information, the authors retrain a convolutional neural network (CNN) to build up their pixel-level potentials. Then, to obtain abundant spatial segmentation information in patch level, they fine tune another three CNNs to build up their patch-level potentials. Thirdly, based on the pixel- and patch-level potentials, their HCRF model is structured. Finally, a graph-based post-processing is applied to further improve their segmentation performance. In the experiment, a segmentation accuracy of 78.91 % is achieved on a haematoxylin and eosin stained gastric histopathological dataset with 560 images, showing the effectiveness and future potential of the proposed GHIS method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text