This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region histogram of the label distributions. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.
In the scope of Gram stain test, most related works @cite_30 @cite_14 @cite_15 only aim to detect and count the existence of different cells and bacteria. Despite the fact that progress has been made, these works assume that the areas of interest have been pre-selected by experts. As such, the fast automatic area of interest selection @cite_22 in low magnitude objectives is becoming an important step to have a fully automated CAD system for this application. The reason is that the final analysis is done by using high-powered objectives (e.g. 100x) which involves extremely long scanning times, huge image storage, and long processing times. So in the real scenario, it is a common strategy to choose a subset of good regions in low-mag to zoom into for further high-mag analysis.
@cite_30 @cite_15 @cite_14 @cite_22
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The field of bioinformatics shows a tremendous growth at the crossroads of biology, medicine, and informatics. Applying data mining techniques in the medical field is a very challenging undertaking due to the idiosyncrasies of the medical profession. Using conventional methods, it is very difficult to determine the exact number of microorganisms in a microscopic picture. In this paper, the emphasis is on the automatic detection of microbes using automated tools and extraction of bacterial clusters through statistical and neural network approaches. Also, Multiscan approaches with freeman chain code and contour detection for the bacterial patterns in the images have been presented. Experimental results shows that the bacterial cluster patterns obtained through neural network approach are better than the statistical approach. Microscope examination of Gram stained clinical specimens is used for aiding the diagnosis of patients with infectious diseases. In high volume pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. Unfortunately, despite the great benefits offered from the application of Computer Aided Diagnosis (CAD) systems, to our knowledge, the highest automation stage for Gram stained slide analysis is only at the pre-analytical process. This paper takes the first steps towards the application of computer vision to direct smear, Gram stained images. To that end, we present a novel Gram stain image dataset. In addition, we also propose a multiple covariance approach for leukocyte and epithelial cell detection in Gram stain images. Each covariance matrix represents a particular image region characterising the cell's deformed structure. As covariance matrices form points on an Symmetric Positive Definite (SPD) manifold, the traditional Euclidean-based analysis cannot be used. As such, we first map the manifold points into the Reproducing Kernel Hilbert Space (RKHS). The analysis is done via a novel kernel similarity function that allows comparison between sets of covariance matrices. The proposed approach is contrasted, in the proposed dataset, with two recent state of the art methods in pedestrian detection: Histogram Of Gradient (HOG) and the traditional single covariance matrix approach. We found that the proposed approach outperformed both of these methods. Gram stained direct smears test is clinically useful in early identification of infections. Unfortunately, this practice is considered time consuming and labour intensive. Most existing effort in this area is to perform highmagnification analysis of images taken from manually selected areas. In this paper, we address the problem of the automatic selection of candidate areas (or patches) for subsequent high-magnification analysis. Specifically, we explore and study the possibility of selecting good working areas based on low-magnification images, where bacteria are likely to be found when viewed in high-magnification images. To this end, we develop an approach to classify the areas of interest according to the textural information of the image patch. We explore and study the efficacy of traditional textural features such as Histogram of Gradients, Local Binary Patterns, and 2 Dimensional Discrete Cosine Transform. Experiments show that the best variant method is able to select working areas where it is likely to find bacteria in high-powered objective images in a wide range of images.