Gastric histopathology image segmentation using a hierarchical conditional random field

03/03/2020
by   Changhao Sun, et al.
0

In this paper, a Hierarchical Conditional Random Field (HCRF) model based Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can localize abnormal (cancer) regions in gastric histopathology images obtained by optical microscope to assist histopathologists in medical work. First, to obtain pixel-level segmentation information, we retrain a Convolutional Neural Network (CNN) to build up our pixel-level potentials. Then, in order to obtain abundant spatial segmentation information in patch-level, we fine-tune another three CNNs to build up our patch-level potentials. Thirdly, based on the pixel- and patch-level potentials, our HCRF model is structured. Finally, graph-based post-processing is applied to further improve our segmentation performance. In the experiment, a segmentation accuracy of 78.91 and Eosin (H E) stained gastric histopathological dataset with 560 images, showing the effectiveness and future potential of the proposed GHIS method.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 22

page 24

page 27

page 29

page 30

page 32

page 34

03/08/2020

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

In order to assist researchers to identify Environmental Microorganisms ...
02/21/2021

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

In the Gastric Histopathology Image Classification (GHIC) tasks, which i...
07/08/2020

A Multi-Level Approach to Waste Object Segmentation

We address the problem of localizing waste objects from a color image an...
03/11/2022

Hyperbolic Image Segmentation

For image segmentation, the current standard is to perform pixel-level o...
09/05/2017

Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions

We introduce a dynamic multiscale tree (DMT) architecture that learns ho...
12/21/2015

Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs

Our aim is to provide a pixel-wise instance-level labeling of a monocula...
05/19/2016

Hierarchical Piecewise-Constant Super-regions

Recent applications in computer vision have come to heavily rely on supe...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.