Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems

注目領域検出のための視覚的注意モデル設計に関する研究

Visual attention is an important mechanism in the human visual system. When human observe images and videos, they usually do not describe all the contents in them. Instead, they tend to talk about the semantically important regions and objects in the images. The human eye is usually attracted by some regions of interest rather than the entire scene. These regions of interest that present the mainly meaningful or semantic content are called saliency region. Visual saliency detection refers to the use of intelligent algorithms to simulate human visual attention mechanism, extract both the low-level features and high-level semantic information and localize the salient object regions in images and videos. The generated saliency map indicates the regions that are likely to attract human attention. As a fundamental problem of image processing and computer vision, visual saliency detection algorithms have been extensively studied by researchers to solve practical tasks, such as image and video compression, image retargeting, object detection, etc. The visual attention mechanism adopted by saliency detection in general are divided into two categories, namely the bottom-up model and top-down model. The bottom-up attention algorithm focuses on utilizing the low-level visual features such as colour and edges to locate the salient objects. While the top-down attention utilizes the supervised learning to detect saliency. In recent years, more and more research tend to design deep neural networks with attention mechanisms to improve the accuracy of saliency detection. The design of deep attention neural network is inspired by human visual attention. The main goal is to enable the network to automatically capture the information that is critical to the target tasks and suppress irrelevant information, shift the attention from focusing on all to local. Currently various domain’s attention has been developed for saliency detection and semantic segmentation, such as the spatial attention module in convolution network, it generates a spatial attention map by utilizing the inter-spatial relationship of features; the channel attention module produces a attention by exploring the inter-channel relationship of features. All these well-designed attentions have been proven to be effective in improving the accuracy of saliency detection. This paper investigates the visual attention mechanism of salient object detection and applies it to digital histopathology image analysis for the detection and classification of breast cancer metastases. As shown in following contents, the main research contents include three parts: First, we studied the semantic attention mechanism and proposed a semantic attention approach to accurately localize the salient objects in complex scenarios. The proposed semantic attention uses Faster-RCNN to capture high-level deep features and replaces the last layer of Faster-RCNN by a FC layer and sigmoid function for visual saliency detection; it calculates proposals' attention probabilities by comparing their feature distances with the possible salient object. The proposed method introduces a re-weighting mechanism to reduce the influence of the complexity background, and a proposal selection mechanism to remove the background noise to obtain objects with accurate shape and contour. The simulation result shows that the semantic attention mechanism is robust to images with complex background due to the consideration of high-level object concept, the algorithm achieved outstanding performance among the salient object detection algorithms in the same period. Second, we designed a deep segmentation network (DSNet) for saliency object prediction. We explored a Pyramidal Attentional ASPP (PA-ASPP) module which can provide pixel level attention. DSNet extracts multi-level features with dilated ResNet-101 and the multiscale contextual information was locally weighted with the proposed PA-ASPP. The pyramid feature aggregation encodes the multi-level features from three different scales. This feature fusion incorporates neighboring scales of context features more precisely to produce better pixel-level attention. Finally, we use a scale-aware selection (SAS) module to locally weight multi-scale contextual features, capture important contexts of ASPP for the accurate and consistent dense prediction. The simulation results demonstrated that the proposed PA-ASPP is effective and can generate more coherent results. Besides, with the SAS, the model can adaptively capture the regions with different scales effectively. Finally, based on previous research on attentional mechanisms, we proposed a novel Deep Regional Metastases Segmentation (DRMS) framework for the detection and classification of breast cancer metastases. As we know, the digitalized whole slide image has high-resolution, usually has gigapixel, however the size of abnormal region is often relatively small, and most of the slide region are normal. The highly trained pathologists usually localize the regions of interest first in the whole slide, then perform precise examination in the selected regions. Even though the process is time-consuming and prone to miss diagnosis. Through observation and analysis, we believe that visual attention should be perfectly suited for the application of digital pathology image analysis. The integrated framework for WSI analysis can capture the granularity and variability of WSI, rich information from multi-grained pathological image. We first utilize the proposed attention mechanism based DSNet to detect the regional metastases in patch-level. Then, adopt the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to predict the whole metastases from individual slides. Finally, determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low-and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach