sivq-lcm-protocol-for-the-arcturusxt-instrument

SIVQ-LCM Protocol for the ArcturusXT Instrument

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process.&#160;It aims to create an ...

University of Michigan

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HISTORICALLY, EFFECTIVE CLINICAL UTILIZATION OF IMAGE ANALYSIS AND PATTERN RECOGNITION ALGORITHMS IN PATHOLOGY HAS BEEN HAMPERED BY TWO CRITICAL LIMITATIONS: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise.

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.

HISTORIQUEMENT, utilisation efficace CLINIQUE DE L&#x26;#39;ANALYSE D&#x26;#39;IMAGE ET ALGORITHMES reconnaissance des formes en pathologie a &#xE9;t&#xE9; entrav&#xE9;e par deux limites critiques: 1) la disponibilit&#xE9; des ensembles de donn&#xE9;es num&#xE9;riques enti&#xE8;res d&#x26;#39;imagerie de diapositives et 2) un d&#xE9;ficit relatif des connaissances du domaine en termes d&#x26;#39;application de ces algorithmes, le la partie de la pratique des pathologistes. Avec l&#x26;#39;av&#xE8;nement de l&#x26;#39;adoption r&#xE9;cente et rapide de l&#x26;#39;ensemble des solutions d&#x26;#39;imagerie de diapositives, la limitation ancien a &#xE9;t&#xE9; en grande partie r&#xE9;solu. Cependant, avec l&#x26;#39;espoir qu&#x26;#39;il est peu probable pour la cohorte g&#xE9;n&#xE9;rale des pathologistes contemporains d&#x26;#39;acqu&#xE9;rir des comp&#xE9;tences avanc&#xE9;es en analyse d&#x26;#39;images dans le court terme, le dernier probl&#xE8;me demeure, soulignant ainsi la n&#xE9;cessit&#xE9; d&#x26;#39;une classe d&#x26;#39;algorithme qui poss&#xE8;de les propri&#xE9;t&#xE9;s simultan&#xE9;es de domaine de l&#x26;#39;image ( ou syst&#xE8;mes d&#x26;#39;organes) et l&#x26;#39;ind&#xE9;pendance extr&#xEA;me facilit&#xE9; d&#x26;#39;utilisation, sans la n&#xE9;cessit&#xE9; d&#x26;#39;une formation sp&#xE9;cialis&#xE9;e ou de l&#x26;#39;expertise.

Vector Quantization spatialement invariants: Un algorithme de Pattern Matching pour les classes multiples de mati&#xE8;re d&#x26;#39;image notamment la pathologie

HISTORISCH, effektive klinische Nutzung von Bild Analyse und Muster Anerkennung Algorithmen IN Pathologie hat wurde behindert von zwei kritische Einschr&auml;nkungen: 1) die Verf&uuml;gbarkeit von digitalen ganze Folie Bilder von Datens&auml;tzen und 2) eine relative Dom&auml;ne wissen Defizit in Bezug auf die Anwendung solcher Algorithmen, seitens der Pathologen zu &uuml;ben. Mit dem Aufkommen der j&uuml;ngsten und schnelle Annahme des ganzen Folie-imaging-L&ouml;sungen hat die ehemalige Beschr&auml;nkung weitgehend gel&ouml;st. Bleibt jedoch mit der Erwartung, dass es unwahrscheinlich f&uuml;r die allgemeine Kohorte der zeitgen&ouml;ssischen Pathologen erweiterte Bild-Analyse-F&auml;higkeiten kurzfristig zu gewinnen ist, das letztere Problem, so unterstreicht die Notwendigkeit einer Klasse des Algorithmus, der die gleichzeitige Eigenschaften des Bild-Dom&auml;ne (oder Organsystem) Unabh&auml;ngigkeit hat und extreme Benutzerfreundlichkeit, ohne spezielle Ausbildung oder Fachwissen.

R&auml;umlich invariante Vektorquantisierung: A Muster &uuml;bereinstimmenden Algorithmus f&uuml;r mehrere Klassen von Bild-Gegenstand, einschlie&szlig;lich Pathologie.

STORICAMENTE, efficace clinico utilizzo di immagine analisi e PATTERN riconoscimento algoritmi IN patologia ha stato ostacolato da due critici limitazioni: 1) la disponibilit&agrave; di insiemi di dati di immagini digitali intera diapositiva e 2) un deficit di conoscenza del relativo dominio in termini di applicazione di tali algoritmi, da parte di patologi la pratica. Con l&#39;avvento della recente e rapida adozione di soluzioni di imaging intera diapositiva, la limitazione ex &egrave; stato in gran parte risolto. Tuttavia, con l&#39;aspettativa che &egrave; improbabile per il generale coorte dei patologi contemporanei ad acquisire competenze di analisi avanzate immagine nel breve termine, il problema di quest&#39;ultimo rimane, sottolineando cos&igrave; la necessit&agrave; di una classe dell&#39;algoritmo che ha l&#39;indipendenza simultanee propriet&agrave; del dominio di immagine (o organo) ed estrema facilit&agrave; di utilizzo, senza la necessit&agrave; di una formazione specializzata o perizia.

Quantizzazione vettoriale spazialmente invarianti: A pattern algoritmo di matching per pi&ugrave; classi di immagine materia compresa la patologia.

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HIST&#xD3;RICAMENTE, la utilizaci&#xF3;n eficaz CL&#xCD;NICA DE AN&#xC1;LISIS DE IM&#xC1;GENES Y ALGORITMOS el reconocimiento de patrones en Patolog&#xED;a ha visto obstaculizada por dos limitaciones serias: 1) la disponibilidad de datos digitales completos de diapositivas de im&#xE1;genes juegos y 2) un d&#xE9;ficit relativo conocimiento del dominio en t&#xE9;rminos de aplicaci&#xF3;n de este tipo de algoritmos, en por parte de la pr&#xE1;ctica de los pat&#xF3;logos. Con el advenimiento de la reciente adopci&#xF3;n y r&#xE1;pida de las soluciones enteras de diapositivas de imagen, la limitaci&#xF3;n anterior se ha resuelto en gran medida. Sin embargo, con la expectativa de que es poco probable para la cohorte general de pat&#xF3;logos contempor&#xE1;neos para obtener conocimientos avanzados de an&#xE1;lisis de imagen en el corto plazo, el problema sigue siendo este &#xFA;ltimo, lo que subraya la necesidad de una clase de algoritmo que tiene las propiedades concurrentes de dominio de la imagen ( o de un &#xF3;rgano del sistema) la independencia y la extrema facilidad de uso, sin la necesidad de formaci&#xF3;n especializada o experiencia.

Vector espacial invariante Cuantificaci&#xF3;n: Un algoritmo de Coincidencia de patrones de m&#xFA;ltiples clases de la materia Imagen tema, incluyendo Patolog&#xED;a

Laser capture microdissection (LCM) facilitates procurement of defined cell populations for study in the context of histopathology. The morphologic assessment step in the LCM procedure is time consuming and tedious, thus restricting the utility of the technology for large applications.

SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling.

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Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess.

Assist&#xE9;e par Ordinateur Outils de diagnostic visent &#xE0; permettre aux Plut&#xF4;t que de remplacer pathologistes: Le&#xE7;ons tir&#xE9;es de Chess informatique

Computergest&uuml;tzte Diagnose-Tools Ziel zu erm&auml;chtigen, anstatt Sie zu ersetzen Pathologen: Lehren aus der computational Schach.

Computer aided strumenti diagnostici obiettivo rafforzare piuttosto che sostituire i patologi: lezioni apprese da scacchi computazionali.

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Asistido por Computadora Herramientas de diagn&#xF3;stico se pretende capacitar a lugar de reemplazar Pat&#xF3;logos: Lecciones Aprendidas de ajedrez computacional

The need for the pathology community to sponsor a whole slide imaging repository with technical guidance from the pathology informatics community.

Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector's sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count.

Automated vector selection of SIVQ and parallel computing integration MATLAB™: Innovations supporting large-scale and high-throughput image analysis studies.

For personalization of medicine, increasingly clinical and demographic data are integrated into nomograms for prognostic use, while molecular biomarkers are being developed to add independent diagnostic, prognostic, or management information. In a number of cases in surgical pathology, morphometric quantitation is already performed manually or semi-quantitatively, with this effort contributing to diagnostic workup. Digital whole slide imaging, coupled with emerging image analysis algorithms, offers great promise as an adjunctive tool for the surgical pathologist in areas of screening, quality assurance, consistency, and quantitation. We have recently reported such an algorithm, SIVQ (Spatially Invariant Vector Quantization), which avails itself of the geometric advantages of ring vectors for pattern matching, and have proposed a number of potential applications. One key test, however, remains the need for demonstration and optimization of SIVQ for discrimination between foreground (neoplasm- malignant epithelium) and background (normal parenchyma, stroma, vessels, inflammatory cells). Especially important is the determination of relative contributions of each key SIVQ matching parameter with respect to the algorithm's overall detection performance. Herein, by combinatorial testing of SIVQ ring size, sub-ring number, and inter-ring wobble parameters, in the setting of a morphologically complex bladder cancer use case, we ascertain the relative contributions of each of these parameters towards overall detection optimization using urothelial carcinoma as a use case, providing an exemplar by which this algorithm and future histology-oriented pattern matching tools may be validated and subsequently, implemented broadly in other appropriate microscopic classification settings.

Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm.

Recently, with the advent of the 7th edition of the AJCC Cancer Staging manual, at least one set of criteria (e.g. breast) were modified to now require the measurement of maximal depth of stromal invasion. With the current manual interpretive morphological approaches typically employed by surgical pathologists to assess tumor extent, the specialty now potentially has stumbled upon a crossroads of practice, where the diagnostic criteria have exceeded the capabilities of our commonly available tools. While whole slide imaging (WSI) technology holds the potential to offer many improvements in clinical workflow over conventional slide microscopy including unambiguous utility for facilitating quantitative diagnostic tasks with one important example being the determination of both linear dimension and surface area. However, the availability of histology data in digital form is of little utility if time-consuming and cumbersome manual workflow steps are necessarily imposed upon the pathologist in order to generate such measurements, especially as encountered with the complex and ill-defined shapes inherent to infiltrative tumors. In this communication, we demonstrate the utility of the recently described SIVQ algorithm to serve as the basis of a highly accurate, precise and semi-automated tool for direct surface area measurement of tumor infiltration from WSI data sets. By anticipating the current trend in cancer staging that emphasizes increasingly precise feature characterization, as witnessed by the recent publication of AJCC's 7th edition of the Cancer Staging Manual, this tool holds promise to will be of value to pathologists for clinical utility.

Automated area calculation of histopathologic features using SIVQ.

The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository.

Image microarrays (IMA): Digital pathology's missing tool.

The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves. This requires a pathologist to annotate, or paint, each of the malignant glands in prostate cancer with an image editor software - a time consuming and exhaustive process. Recently, two CAD algorithms have been described: probabilistic pairwise Markov models (PPMM) and spatially-invariant vector quantization (SIVQ). Briefly, SIVQ operates as a highly sensitive and specific pattern matching algorithm, making it optimal for the identification of any epithelial morphology, whereas PPMM operates as a highly sensitive detector of malignant perturbations in glandular lumenal architecture.

Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification.

In the past decade molecular diagnostics has changed the clinical management of lung adenocarcinoma patients. Molecular diagnostics, however, is largely dependent on the quantity and quality of the tumor DNA that is retrieved from the tissue or cytology samples. Frequently, patients are diagnosed on cytology specimens where the tumor cells are scattered within the cell block, making selecting for tumor enrichment difficult. In the past we have used laser capture microdissection (LCM) to select for pure populations of tumor cells to increase the sensitivity of molecular assays. This study explores several methods for semiautomated computer-guided LCM.

Jerome Cheng

Department of Pathology

University of Michigan

SIVQ-LCM Protocol for the ArcturusXT Instrument

Jerome Cheng

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Pathology

University of Michigan

SIVQ-LCM Protocol for the ArcturusXT Instrument

Department of Pathology