How to measure diagnosis-associated information in virtual slides

The distribution of diagnosis-associated information in histological slides is often spatial dependent. A reliable selection of the slide areas containing the most significant information to deriving the associated diagnosis is a major task in virtual microscopy. Three different algorithms can be used to select the appropriate fields of view: 1) Object dependent segmentation combined with graph theory; 2) time series associated texture analysis; and 3) geometrical statistics based upon geometrical primitives. These methods can be applied by sliding technique (i.e., field of view selection with fixed frames), and by cluster analysis. The implementation of these methods requires a standardization of images in terms of vignette correction and gray value distribution as well as determination of appropriate magnification (method 1 only). A principle component analysis of the color space can significantly reduce the necessary computation time. Method 3 is based upon gray value dependent segmentation followed by graph theory application using the construction of (associated) minimum spanning tree and Voronoi’s neighbourhood condition. The three methods have been applied on large sets of histological images comprising different organs (colon, lung, pleura, stomach, thyroid) and different magnifications, The trials resulted in a reproducible and correct selection of fields of view in all three methods. The different algorithms can be combined to a basic technique of field of view selection, and a general theory of “image information” can be derived. The advantages and constraints of the applied methods will be discussed

Grid technology in tissue-based diagnosis: fundamentals and potential developments

Tissue-based diagnosis still remains the most reliable and specific diagnostic medical procedure. It is involved in all technological developments in medicine and biology and incorporates tools of quite different applications. These range from molecular genetics to image acquisition and recognition algorithms (for image analysis), or from tissue culture to electronic communication services. Grid technology seems to possess all features to efficiently target specific constellations of an individual patient in order to obtain a detailed and accurate diagnosis in providing all relevant information and references. Grid technology can be briefly explained by so-called nodes that are linked together and share certain communication rules in using open standards. The number of nodes can vary as well as their functionality, depending on the needs of a specific user at a given point in time. In the beginning of grid technology, the nodes were used as supercomputers in combining and enhancing the computation power. At present, at least five different Grid functions can be distinguished, that comprise 1) computation services, 2) data services, 3) application services, 4) information services, and 5) knowledge services. The general structures and functions of a Grid are described, and their potential implementation into virtual tissue-based diagnosis is analyzed. As a result Grid technology offers a new dimension to access distributed information and knowledge and to improving the quality in tissue-based diagnosis and therefore improving the medical quality

Theory of sampling and its application in tissue based diagnosis

<p>Abstract</p> <p>Background</p> <p>A general theory of sampling and its application in tissue based diagnosis is presented. Sampling is defined as extraction of information from certain limited spaces and its transformation into a statement or measure that is valid for the entire (reference) space. The procedure should be reproducible in time and space, i.e. give the same results when applied under similar circumstances. Sampling includes two different aspects, the procedure of sample selection and the efficiency of its performance. The practical performance of sample selection focuses on search for localization of specific compartments within the basic space, and search for presence of specific compartments.</p> <p>Methods</p> <p>When a sampling procedure is applied in diagnostic processes two different procedures can be distinguished: I) the evaluation of a diagnostic significance of a certain object, which is the probability that the object can be grouped into a certain diagnosis, and II) the probability to detect these basic units. Sampling can be performed without or with external knowledge, such as size of searched objects, neighbourhood conditions, spatial distribution of objects, etc. If the sample size is much larger than the object size, the application of a translation invariant transformation results in Kriege's formula, which is widely used in search for ores. Usually, sampling is performed in a series of area (space) selections of identical size. The size can be defined in relation to the reference space or according to interspatial relationship. The first method is called random sampling, the second stratified sampling.</p> <p>Results</p> <p>Random sampling does not require knowledge about the reference space, and is used to estimate the number and size of objects. Estimated features include area (volume) fraction, numerical, boundary and surface densities. Stratified sampling requires the knowledge of objects (and their features) and evaluates spatial features in relation to the detected objects (for example grey value distribution around an object). It serves also for the definition of parameters of the probability function in so – called active segmentation.</p> <p>Conclusion</p> <p>The method is useful in standardization of images derived from immunohistochemically stained slides, and implemented in the EAMUS™ system <url>http://www.diagnomX.de</url>. It can also be applied for the search of "objects possessing an amplification function", i.e. a rare event with "steering function". A formula to calculate the efficiency and potential error rate of the described sampling procedures is given.</p

Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology)

© 2008 Kayser et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Interactive and automated application of virtual microscopy

Virtual microscopy can be applied in an interactive and an automated manner. Interactive application is performed in close association to conventional microscopy. It includes image standardization suitable to the performance of an individual pathologist such as image colorization, white color balance, or individual adjusted brightness. The steering commands have to include selection of wanted magnification, easy navigation, notification, and simple measurements (distances, areas). The display of the histological image should be adjusted to the physical limits of the human eye, which are determined by a view angle of approximately 35 seconds. A more sophisticated performance should include acoustic commands that replace the corresponding visual commands. Automated virtual microscopy includes so-called microscopy assistants which can be defined similar to the developed assistants in computer based editing systems (Microsoft Word, etc.). These include an automated image standardization and correction algorithms that excludes images of poor quality (for example uni-colored or out-of-focus images), an automated selection of the most appropriate field of view, an automated selection of the best magnification, and finally proposals of the most probable diagnosis. A quality control of the final diagnosis, and feedback to the laboratory determine the proposed system. The already developed tools of such a system are described in detail, as well as the results of first trials. In order to enhance the speed of such a system, and to allow further user-independent development a distributed implementation probably based upon Grid technology seems to be appropriate. The advantages of such a system as well as the present pathology environment and its expectations will be discussed in detail

Grid computing in image analysis

Diagnostic surgical pathology or tissue–based diagnosis still remains the most reliable and specific diagnostic medical procedure. The development of whole slide scanners permits the creation of virtual slides and to work on so-called virtual microscopes. In addition to interactive work on virtual slides approaches have been reported that introduce automated virtual microscopy, which is composed of several tools focusing on quite different tasks. These include evaluation of image quality and image standardization, analysis of potential useful thresholds for object detection and identification (segmentation), dynamic segmentation procedures, adjustable magnification to optimize feature extraction, and texture analysis including image transformation and evaluation of elementary primitives

Cognitive Algorithms and digitized Tissue – based Diagnosis

Aims: To analyze the nature and impact of cognitive algorithms and programming on digitized tissue – based diagnosis. Definitions: Digitized tissue – based diagnosis includes all computerized tissue investigations that contribute to the most appropriate description and forecast of the actual patient’s disease [1]. Cognitive algorithms are programs that encompass machine learning, reasoning, and human – computer interaction [2]. Theoretical considerations: Digitized blood data, objective clinical findings, microscopic, gross, radiological images and gene alterations are analyzed by specialized image analysis methods, and transferred in numbers and vectors. These are analyzed by statistical procedures. They include higher order statistics such as multivariate analysis, neural networks and ‘black box’ strategies, for example ‘deep learning’ or ‘Watson’ approaches. These algorithms can be applied at different cognitive ‘levels’, to reach a digital decision for different procedures which should assist the patient’s health condition. These levels can be grouped in self learning, self promoting, self targeting, and self exploring algorithms. Each of them requires a memory and neighbourhood condition. Self targeting and exploring algorithms are circumscribed mechanisms with singularities and repair procedures. They develop self recognition.   Consecutives: Medical doctors including pathologists are commonly not trained to understand the basic principles and workflow of applied or potential future procedures. At present, basic medical data only serve for simple cognitive algorithms. Most of the investigations focus on ‘deep learning’ procedures. The applied learning and decision algorithms might be modified and themselves be used for ‘next order cognitive algorithms’. Such systems will develop their own strategies, and become independent from potential human interactions. The basic strategy of such IT systems is described herein. Perspectives: Medical doctors including pathologists should be aware about the abilities to enhance their work by supporting tools. In some case the users may not be able to fully understand these tools. Furthermore, these tools will probably become self learning, and, therefore, seem to propose the daily workflow probably without any medical control or even interaction

Radiation-induced leiomyosarcoma of the oropharynx

Leiomyosarcoma is a malignant mesenchymal tumor originating from smooth muscle cells, which most frequently develops in the myometrium and in the gastro-intestinal tract. Reviewing the international literature, radiation-induced sarcoma arise in 0.035 to 0.2 % of all irradiated patients. Especially in the head and neck region, radiation-induced leiomyosarcoma is an extremely rare lesion. The authors report a case of a radiation-induced leiomyosarcoma of the tonsillar region of the oropharynx in a 51-year-old male patient, who had undergone radiation therapy of this region 38 years before. The lesion was treated by radical surgery. Diagnostic steps, histological presentation and therapy are described in detail and the literature concerning radiation induced malignancies in general as well as radiation induced leiomyosarcoma in particular is reviewed. The highlights of this case are an extremely uncommon location and a rare pathological entity of radiation induced malignancies