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

Kayser, Klaus; Görtler, Jürgen; Goldmann, Torsten; Vollmer, Ekkehard; Hufnagl, Peter; Kayser, Gian

doi:10.1186/1746-1596-3-17

Cited by 47 publications

(35 citation statements)

References 57 publications

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“…Texture derived parameters strongly depend upon the image size, which should be kept constant within the system [13,14]. Dependent upon the available computation power the image size could be set to 2 k×2 k pixel; however an image size of 0.5 k ×0.5 k pixels was sufficient in our measurements [15].…”

Section: Discussionmentioning

confidence: 99%

“…There do several algorithms exist how to compare images in between, most of them are designed to assure good quality image copies. [10,13,14]. Virtual slides often do not fulfil a homogenous illumination, and might be too "dark" or too pale dependent upon the staining procedure and scanner assessment.…”

Section: Image Standardization Proceduresmentioning

confidence: 99%

“…Structure is created by the spatial arrangement of objects, and requires external knowledge. Texture is a pixel based gray value distribution and not related to any external limitations or setups [13,15]. The definition of a texture is often crude and not reproducible [19], detailed definitions are related to recursive formulas or locally dependent and independent image transformations [27].…”

Section: Texture Analysismentioning

confidence: 99%

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

Kayser

Görtler²,

Bogovac³

et al. 2010

Folia Histochem Cytobiol.

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Abstract: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.

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Section: Discussionmentioning

confidence: 99%

Section: Image Standardization Proceduresmentioning

confidence: 99%

Section: Texture Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Kayser

Görtler²,

Bogovac³

et al. 2010

Folia Histochem Cytobiol.

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“…The actual pathology information system will also change to allow storage and retrieval of the digital slides, as well as include diagnosis databases linked to the images [22,23]. According to the workflow, several grid functions may be distinguished in at least five different grid levels [24]. These levels are:…”

Section: Methodsmentioning

confidence: 99%

Image processing methods and architectures in diagnostic pathology.

Bueno

Déniz²,

Jesus³

et al. 2010

Folia Histochem Cytobiol.

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Grid technology has enabled the clustering and the efficient and secure access to and interaction among a wide variety of geographically distributed resources such as: supercomputers, storage systems, data sources, instruments and special devices and services. Their main applications include large-scale computational and data intensive problems in science and engineering. General grid structures and methodologies for both software and hardware in image analysis for virtual tissue-based diagnosis has been considered in this paper. This methods are focus on the user level middleware. The article describes the distributed programming system developed by the authors for virtual slide analysis in diagnostic pathology. The system supports different image analysis operations commonly done in anatomical pathology and it takes into account secured aspects and specialized infrastructures with high level services designed to meet application requirements. Grids are likely to have a deep impact on health related applications, and therefore they seem to be suitable for tissue-based diagnosis too. The implemented system is a joint application that mixes both Web and Grid Service Architecture around a distributed architecture for image processing. It has shown to be a successful solution to analyze a big and heterogeneous group of histological images under architecture of massively parallel processors using message passing and non-shared memory.

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“…Th e system developed by Mireskandari et al 1 is appropriate for and has been tested in telepathology sessions to grade diff erences between primary and secondary diagnosis. Th e procedure can be embedded into a general hierarchic standardization method which includes three levels {aim, methodology, and specifi c limitations} 2 . Th ree diff erent "columns", i.e., legal, medical, and technical aspects can be implemented into the hierarchy.…”

Section: Basic Conceptionmentioning

confidence: 99%