Int J Comput Assist Radiol Surg - Cognition Network Technology prototype of a CAD system for mammography to assist radiologists by finding similar cases in a reference database.


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RPOSE: We present a new approach for computer-aided detection and diagnosis in mammography based on Cognition Network Technology (CNT). Originally designed for image processing, CNT has been extended to also perform context- and knowledge-driven analysis of tabular data. For the first time using this technology, an application was created and evaluated for fully automatic searching of patient cases from a reference database of verified findings. The application aims to support radiologists in providing cases of similarity and relevance to a given query case. It adopts an extensible and knowledge-driven concept as a similarity measure.METHODS: As a preprocessing step, all input images from more than 400 patients were fully automatically segmented and the resulting objects classified--this includes the complete breast shape, the position of the mammilla, the pectoral muscle, and various potential candidate objects for suspicious mass lesions. For the similarity search, collections of object properties and metadata from many patients were combined into a single table analysis project. Extended CNT allows for a convenient implementation of knowledge-based structures, for example, by meaningfully linking detected objects in different breast views that might represent identical lesions. Objects from alternative segmentation methods are also be considered, so as to collectively become a sufficient set of base-objects for identifying suspicious mass lesions.RESULTS: For 80% of 112 patient cases with suspicious lesions, the system correctly identified at least one corresponding mass lesion as an object of interest. In this database, consisting of 1,024 images from a total of 303 patients, an average of 0.66 false-positive objects per image were detected. An additional testing database contained 480 images from 120 patients, 15 of whom were annotated with suspicious mass lesions. Here, 47% (7 out of 15) of these were detected automatically with 1.13 false-positive objects per image. A diagnosis is predicted for each patient case by applying a majority vote from the reference findings of the ten most similar cases. Two separate evaluation scenarios suggest a fraction of correct predictions of respectively 79 and 76%.CONCLUSION: Cognition Network Technology was extended to process table data, making it possible to access and relate records from different images and non-image sources, such as demographic patient data or parameters from clinical examinations. A prototypal application enables efficient searching of a patient and image database for similar patient cases. Using concepts of knowledge-driven configuration and flexible extension, the application illustrates a path to a new generation of future CAD systems.

Resumo Limpo

rpose present new approach computeraid detect diagnosi mammographi base cognit network technolog cnt origin design imag process cnt extend also perform context knowledgedriven analysi tabular data first time use technolog applic creat evalu fulli automat search patient case refer databas verifi find applic aim support radiologist provid case similar relev given queri case adopt extens knowledgedriven concept similar measuremethod preprocess step input imag patient fulli automat segment result object classifiedthi includ complet breast shape posit mammilla pector muscl various potenti candid object suspici mass lesion similar search collect object properti metadata mani patient combin singl tabl analysi project extend cnt allow conveni implement knowledgebas structur exampl meaning link detect object differ breast view might repres ident lesion object altern segment method also consid collect becom suffici set baseobject identifi suspici mass lesionsresult patient case suspici lesion system correct identifi least one correspond mass lesion object interest databas consist imag total patient averag falseposit object per imag detect addit test databas contain imag patient annot suspici mass lesion detect automat falseposit object per imag diagnosi predict patient case appli major vote refer find ten similar case two separ evalu scenario suggest fraction correct predict respect conclus cognit network technolog extend process tabl data make possibl access relat record differ imag nonimag sourc demograph patient data paramet clinic examin prototyp applic enabl effici search patient imag databas similar patient case use concept knowledgedriven configur flexibl extens applic illustr path new generat futur cad system

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