gastric segmenting

Researchers from China have developed a hierarchical conditional random field (HCRF) model-based gastric histopathology image segmentation (GHIS) method to localise abnormal (cancerous) regions in gastric histopathology images. A convolutional neural network (CNN) is trained to obtain pixel-level information initially, with another 3 CNNs used to build up patch-level potentials. The HCRF model is based on the work of these 4 CNNs with experiments showing an accuracy of almost 80% when using a dataset of 560 images. Over one million cases of gastric cancer are diagnosed each year, worldwide, with stomach cancer being the 5th most commonly diagnosed cancer overall (GLOBOCAN 2018 data). There is, therefore, a demand for an improvement in screening technologies to catch cancers in early stages. Image analysis is playing a critical role in early diagnoses, with a variety of deep learning methods, especially CNNs, being developed for GHIS tasks. The existing approaches are limited, however, as they tend to focus on individual characteristics and network framework without a strategy to describe the integral information. Workflow of the proposed hierarchical conditional random field model. HCRF segmentation results on validation and test sets. Comparison between image segmentation performance of proposed HCRF model on validation and test sets (in %). Advanced methods to obtain an even better performance have therefore been produced in recent years, integrating individual existing methods. Conditional random fields (CRFs, a class of statistical modelling methods which can be used for pattern recognition and machine learning, and are often used for structured prediction), for example, can characterise the spatial relationships of images and are effective, robust methods for analysing the contents of complex images. Their superiority arises from being able to take context into account when predicting a label for a target image. The work presented in this issue of Electronics Letters is designed to address the main problem with GHIS tasks, namely that histopathological images are mixed with other complex features, such as nucleic, interstitial and tissue fluids. The main contributions of the work are threefold: a heuristically applied CRF framework, a novel hierarchical conditional random field model and a high segmentation performance by said model for gastric histopathology images, where multi-object segmentation is required. Because cellular visual features in a histopathological image can be observed on patch scales, it is better to use patches cut from the image rather than using the whole image when training the classifier. The authors used a public Hematoxylin and Eosin (H&E) stained gastric histopathological image dataset containing 560 abnormal images, along with 560 ground truth (GT) images, to test their proposed model in the GHIS task. The abnormal images were randomly divided into training, validation, and test sets in a ratio of 1:1:2. The training and validation datasets are augmented 6 times. Each abnormal image in the dataset is also cropped to 256 × 256 pixels, down from 2048×2048, overall meaning that there are 53,760 training, 53,760 validation and 107,520 test images. The authors compared their HCRF model with a number of contemporary methods. It was found that their model outperformed all others when it came to factors such as the Sørensen-Dice coefficient, relative volume difference, intersection over union, precision and accuracy. The mean testing time for one 2048 × 2048 pixel image through the whole HCRF model is 61.6 seconds, split into two stages of 28.1 seconds and 33.5 seconds, which is a reasonable timeframe for the method. The authors have certainly proved that their method can work, and is superior in a number of ways to existing methodologies. There is still some way to go in order to have it exceed all other methods in all categories, notably the recall and specificity of the framework when compared to state of the art. Still, it is an innovation that could provide a noticeable improvement in image segmentation techniques when applied to gastric histopathology images.