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

History and structures of telecommunication in pathology, focusing on open access platforms

<p>Abstract</p> <p>Background</p> <p>Telecommunication has matured to a broadly applied tool in diagnostic pathology.</p> <p>Technology and Systems</p> <p>Contemporary with the development of fast electronic communication lines (Integrated digital network services (ISDN), broad band connections, and fibre optics, as well as the digital imaging technology (digital camera), telecommunication in tissue - based diagnosis (telepathology) has matured. Open access (internet) and server - based communication have induced the development of specific medical information platforms, such as iPATH, UICC-TPCC (telepathology consultation centre of the Union International against Cancer), or the Armed Forces Institute of Pathology (AFIP) teleconsultation system. They have been closed, and are subject to be replaced by specific open access forums (Medical Electronic Expert Communication System (MECES) with embedded virtual slide (VS) technology). MECES uses php language, data base driven mySqL architecture, X/L-AMPP infrastructure, and browser friendly W3C conform standards.</p> <p>Experiences</p> <p>The server - based medical communication systems (AFIP, iPATH, UICC-TPCC) have been reported to be a useful and easy to handle tool for expert consultation. Correct sampling and evaluation of transmitted still images by experts reported revealed no or only minor differences to the original images and good practice of the involved experts. β tests with the new generation medical expert consultation systems (MECES) revealed superior results in terms of performance, still image viewing, and system handling, especially as this is closely related to the use of so - called social forums (facebook, youtube, etc.).</p> <p>Benefits and Expectations</p> <p>In addition to the acknowledged advantages of the former established systems (assistance of pathologists working in developing countries, diagnosis confirmation, international information exchange, etc.), the new generation offers additional benefits such as acoustic information transfer, assistance in image screening, VS technology, and teaching in diagnostic sampling, judgement, and verification.</p

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

How to implement virtual microscopy in routine tissue – based diagnosis: Guidelines and Recommendations of the Federal Association of German Pathologists

TMA non small cell lung cancer Liver biopsy LALD (Lysosomal Acid Lipase Deficiency) Dermtofibrosarcoma protuberans Digital Pathology has jumped over the first barriers and enters the world of routine tissue – based diagnosis (surgical pathology) slowly and continuously. Several large pathology institutions use virtual slides (VS) for routine diagnosis,  store the images in digital archives, and digitize their workflow according to the needs of laboratory (LIS) and hospital information system (HIS). Development and implementation of communication standards as well as adequate certification and quality control units are mandatory if an adequate and secure diagnosis and treatment of patients should be maintained or amended. The publication of the translated guidelines of the Federal Association of German Pathologists should give our readers the opportunity to read the well designed and substantial document in its original version.  We include a few representative virtual slides in our editorial in order to demonstrate the actual performance of virtual microscopy. Thus, we offer our readers to comparing some issues of the virtual world with their own situation and to get informed about the essential procedures of accurate virtual microscopy implementation

Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet

AIMS: To develop and implement an automated virtual slide screening system that distinguishes normal histological findings and several tissue – based crude (texture – based) diagnoses. THEORETICAL CONSIDERATIONS: Virtual slide technology has to handle and transfer images of GB Bytes in size. The performance of tissue based diagnosis can be separated into a) a sampling procedure to allocate the slide area containing the most significant diagnostic information, and b) the evaluation of the diagnosis obtained from the information present in the selected area. Nyquist's theorem that is broadly applied in acoustics, can also serve for quality assurance in image information analysis, especially to preset the accuracy of sampling. Texture – based diagnosis can be performed with recursive formulas that do not require a detailed segmentation procedure. The obtained results will then be transferred into a "self-learning" discrimination system that adjusts itself to changes of image parameters such as brightness, shading, or contrast. METHODS: Non-overlapping compartments of the original virtual slide (image) will be chosen at random and according to Nyquist's theorem (predefined error-rate). The compartments will be standardized by local filter operations, and are subject for texture analysis. The texture analysis is performed on the basis of a recursive formula that computes the median gray value and the local noise distribution. The computations will be performed at different magnifications that are adjusted to the most frequently used objectives (*2, *4.5, *10, *20, *40). The obtained data are statistically analyzed in a hierarchical sequence, and in relation to the clinical significance of the diagnosis. RESULTS: The system has been tested with a total of 896 lung cancer cases that include the diagnoses groups: cohort (1) normal lung – cancer; cancer subdivided: cohort (2) small cell lung cancer – non small cell lung cancer; non small cell lung cancer subdivided: cohort (3) squamous cell carcinoma – adenocarcinoma – large cell carcinoma. The system can classify all diagnoses of the cohorts (1) and (2) correctly in 100%, those of cohort (3) in more than 95%. The percentage of the selected area can be limited to only 10% of the original image without any increased error rate. CONCLUSION: The developed system is a fast and reliable procedure to fulfill all requirements for an automated "pre-screening" of virtual slides in lung pathology

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

Extralobular Sequestration

Lung sequestration is a radiological/clinical diagnosis displaying with a mass of lung parenchyma that is not connected to the tracheobronchial tree. Intralobular sequestration defines lung parenchyma that is covered by visceral pleura; extralobular sequestration defines lung parenchyma that lies outside the visceral pleura. We report the histological findings of a 71 years old women suffering from chronic cough, recurrent bronchopneumonias, and several radiological densities in the left lower lobe (lingula). A resection of the lingula was performed. The histological findings include focal active fibrosis and marked media hyperplasia and dislocalization of pulmonary arteries as well as a tumorlet of carcinoid type and focal adenomatous hyperplasia (AAH). Differential diagnosis: Pulmonary Hypertension, Venous occlusive disease, Congenital malformatio

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

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous education in anatomy and pathology. First attempts to introduce them into routine work have been reported. Application of AI has been established by automated immunohistochemical measurement systems (EAMUS, www.diagnomX.eu). The performance of automated diagnosis has been reported for a broad variety of organs at sensitivity and specificity levels >85%). The implementation of a complete connected AI supported system is in its childhood. Application of AI in digital tissue--based diagnosis will allow the pathologists to work as supervisors and no longer as primary "water carriers". Its accurate use will give them the time needed to concentrating on difficult cases for the benefit of their patients