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Sascha Seifert, Siemens Healthcare, pour la journée e-health 2013
1.
Answers for life.Unrestricted
© Siemens AG 2013 All rights reserved. Theseus Medico and imaging in the digital diagnosis Dr. Sascha Seifert eHealth Day Sierre June 2013
2.
Page 2 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 2 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO THESEUS-MEDICO consortium 2 Fraunhofer IGD Siemens CT Erlangen & Munich Averbis Ludwig-Maximillian University Munich University Hospital Erlangen Transinsight DFKI • 11/2007 – 05/2012 • 67.5 man-years, • grant 50% government, 50% industry • 3 research centers, 1 hospital • 3 companies (Siemens & 2 SME) • Lead with Siemens AG
3.
Page 3 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 3 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Codes Control 1980 - 2000 physics Robust Learning Methods Semantic Web Standards 2000-2010 data Open Internet databases Business intelligence Big Data analytics Semantic interoperability 2010 - future content Quelle: Gartner. Hype Cycle for Healthcare Provider Technologies and Standards, July 2010 Theseus-Medico (Semantic Web for Medicine) Google „understands“ now context; knowledge graph with 570 million elements, 18 billion facts, launched in 2012 RSNA.org drives semantic web standard for radiology Evolution of medical data processing Comprehending Software
4.
Page 4 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 4 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO The THESEUS-MEDICO approach Semantic Web1Radiologist / Clinician • Content Understanding • Content Linking • Content Search • Knowledge explosion • Internet, books, articles • Data overload • Unstructured data: Images, Texts • Structured data: Lab values, Medication 1by Tim Berners-Lee
5.
Page 5 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 5 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO
6.
Page 6 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 6 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Application Prototype (National IT-Summit 2011)
7.
Page 7 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 7 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Text Mining FormalizationTextMiningSegmentation Formalization SemanticProcessingofMedicalData Medical Images Radiology Reports Treatment Plans Online Knowledge Expert Knowledge Medical Image Annotation Representational Ontology: OWL Upper Ontology: time, space, organization, person, event moredomainspecific morelikelytobechanged Information Element Ontology images, texts, volumes, … AnnotationOntology Clinical Ontology -doctor, nurse, patient -medical case -DICOM Ontology mid-level ontology low-level ontology Medical Ontologies FMA ICD-10 Website RadLex FMA Exten. Mappings toexternal sources ICD-10 mapping& merging Visual Charac. Mappings toexternal sources annotation Thesauri & Taxonomies extraction Disease-Symptom Navigation Multi-Modal Interaction Quality Control Intelligent Diagnose Ontology EngineeringInformation Extraction from Medical Texts Semantic Search Semantic Reporting Image & Text Linkage Intelligent Healthcare Applications Intelligent applications using knowledge services Knowledge Services Infrastructure Knowledge Extraction and Formalization Knowledge Management Overview Clinical Workflow Components 2,5 TB of 750 patients (≙ ~ 7000 series) 6000 rad reports
8.
Page 8 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 8 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO IMAGE AND TEXT UNDERSTANDING
9.
Page 9 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 9 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Support systems for disease recognition Anatomical Body Regions and Organs Lungs, heart, liver, spleen, kidneys, prostate, urinary bladder, esophagus, pancreas and several anatomical landmarks Valve function, coroanry stenosis, osteolytic tumors, liver tumors, lymph node cancer ImageParsing
10.
Page 10 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 10 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Increase of knowledge about the image content Image Content Detection Segmentation Prior knowledge # Images 3-fold C.V. [mm] Runtime [s/vol] Heart* 457 1,30 3,55 Liver 346 1,07 6,00 Spleen 203 2,14 9,90 Right kidney 199 1,03 0,40 Left kidney 197 1,15 0,40 Left lung 166 2,64 1,70 Richt lung 163 2,35 1,80 Urinary bladder 141 1,35 1,00 8 organs, 19 landmarks, 3 body regions <1 min (09/2010) Generic Image Parsing
11.
Page 11 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 11 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Knowledge based image processing for anatomy understanding Mean error 1,80±1,17 mm Mean error 1,70±0,71 mm Panceas segmentation considering splenic vein i Thoracic lymph nodes Esophagus
12.
Page 12 Unrestricted ©
Siemens AG 2013 All rights reserved. Page 12 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Osteoblastic Osteolytic Total Volumes 30 20 Total Annotations 172 42 False Positives per Patient 3.5 3.7 Overall Sensitivity 83% 88% Mean Sensitivity 80% 93% Overall Positive Predictive Value 58% 35% Mean Positive Predictive Value 65% 49% +9 monthsBaseline Th8 osteolytic volume 10% lower end- plate Th8 mixed volume 95% whole vertebra Fracture progression Th11 osteolytic volume 28% vertebra back new Prior knowledge: vertebrae / discs Constrains lesions search and automatically find corresponding lesions in prior exams Semantic detection and follow-up of spine lesions for bone lesions Results may vary. Data on file
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Siemens AG 2013 All rights reserved. Page 13 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Semantic detection of coronary findings • Automatic vessel tracing and labeling of coronary vessels (Random forest)[Gülsün2008] • Automatic estimation of lumina by Random Forest Regressor (10x faster than segmentation) • Characteristics curves along vessel (e.g., degree of calcification) • Identification of potential stenosis • Additional information such as FFR (measured/simulated) is semantically linked calcified non- calcified mixed overall by lesion sensitivity 96.55% 89.23% 91.78% 94.75% FPR 1.50 2.30 0.87 4.67 by vessel sensitivity 98.67% 94.44% 92.16% 96.47% specificity 79.12% 54.35% 81.39% 71.27% NPV 99.58% 99.21% 99.30% 99.37% 10-fold cross validation with 256 CCTA volumes runtime <2 min / volume Knowledge pipeline: centerlinelumenstenosisclassification Accelerate and quantify readingResults may vary. Data on file
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Siemens AG 2013 All rights reserved. Page 14 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Vessel MEDICO 14 … use knowledge about the anatomy (shape) of the liver to register accurately. Registration of multi modal and multi phase examinations Instead of just comparing grey-values…
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Siemens AG 2013 All rights reserved. Page 15 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGOMEDICO 15 Registration of multi modal and multi phase examinations
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Siemens AG 2013 All rights reserved. Page 16 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGOMEDICO 16 Fusion view of head-neck tomographies Segmenting bones Rigid bone registration “Adherence” of soft tissue Using knowledge about anatomy of bones and soft tissue
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Siemens AG 2013 All rights reserved. Page 17 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Semantic Computer Aided Detection • Horizontal and vertical integration of computer aided detection • Disadvantage of current solutions: • Specific sub systems w/o information exchange / consolidation • „Syntactic“ interoperability Reporting Semantic Search Bone lesion CAD Breast CAD Liver CAD Patient context Bone (e.g. Spine) segmentation THESEUS-MEDICO: Standardized, semantic information using common or (machine-) convertible vocabulary
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Siemens AG 2013 All rights reserved. Page 18 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Three Information Dimensions of Radiology Reports - Challenges 1 Anatomical / Spatial information • Location of a finding, e.g. affected organ, lymph nodes • Spatial modifier, e.g. left, right, axilliary 2 Pathological Information • Pathological interpretation of the highlighted finding, e.g. size (enlarged lymph nodes), density (lung nodule), number of occurrences, etc. 3 Temporal Information • Provides information about the difference/changes of the current findings in relation to past findings, e.g. In comparison to prior examination… Compound words (German): Stemming • Split into sub words before semantic mapping to reduce solution space • Data reduction of 90% in German language Example: Unchanged, not pathologically enlarged axilliary, mediastinal and hilar lymph nodes. FINDINGS:CHEST (EXAMPLE) The lungs demonstrate bilateral areas of pulmonary consolidation, involving predominantly the right upper lobe and to a lesser extent the left upper lobe posterior lingula and superior segment of the lower lobes with additional patchy opacities in the right lung base and right middle lobe. Findings are compatible with multi lobar pneumonia. There are bilateral right greater than left pleural effusions, small in size. There is diffuse anasarca present with 3rd spacing of fluid. There is an old healed displaced right clavicle fracture noted. There are sub centimeter hypodense nodules in both thyroid lobes. There are sub centimeter lymph nodes in the mediastinum measuring up to 9mmin size in the precarinal space, probably reactive. There are calcified lymph nodes in the hila bilaterally, from prior granulomatous disease. There is bibasilar passive atelectasis adjacent to the effusions, with calcified granulomata in both atelectatic lower lobes. The heart is globally enlarged, with coronary artery, aortic valve calcifications present. Additional calcified granulomata are shown in the anterior segment of the right upper lobe. The main pulmonary artery segment is dilated up to 3.3cm in size, suggesting pulmonary artery hypertension. Myo|kard|itis Herz|muskel|entzünd|ung Inflamm|ation of the heart muscle muscle myo muskel muscul inflamm -itis inflam entzünd KONZEPT subwort herz heart card corazon card INFLAMMATION MUSCLE HEART
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Siemens AG 2013 All rights reserved. Page 19 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO 100 report texts from lymphoma cases were manually annotated RadMiner™ Report Search, Averbis GmbH Averbis Annotator (internal) Validation Precision=0.921 Recall=0.935 F1=0.928 Orig: Radlex 2.0 Stem: with stemming Sem: Radlex extended Semantic Report Search Natural Language Processing + Semantic Mapping Results may vary. Data on file
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Siemens AG 2013 All rights reserved. Page 20 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Knowledge Representation Represent segmentations with semantics Manuel Möller, DFKI Kaiserslautern, manuel.moeller@dfki.de Semantic Annotation Medical Ontology Link by Semantic Concepts from Ontology
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Siemens AG 2013 All rights reserved. Page 21 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Annotation Ontology Consolidated data model: knowledge exchange instead of data exchange using semantic web standards (RDF and OWL) new* SPARQL 1.1 (recursive queries) *W3C Working Draft 05 January 2012 Ontology Terms Snomed CT 395036 FMA 83281 Radlex 34895 Reference Ontologies Content Physical Ref. Physical Ref. Reference Ontologies Representation Language The whole picture
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Siemens AG 2013 All rights reserved. Page 22 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO SEMANTIC READING
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Siemens AG 2013 All rights reserved. Page 23 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Semantic Reading – Compare Studies based on Anatomy instead of Frame of Reference Semantics enables registration as expected by radiologists. Locating corresponding anatomical structures by concepts Anatomy is compared with the same anatomy Benefits •synchronized scrolling •compare findings over time •compare similar patients bronchial bifurcation
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Siemens AG 2013 All rights reserved. Page 24 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Text-to-Image Linking (Navigation support) • Hyperlinks encode semantic information and enable to bidirectional jumps • Close semantic gap between image and text based systems. • Improve dialogue of radiologist and clinicians (ideal for radiol. demonstrations, easy access of priors) Anatomy aware findings labeling • Benefit from image understanding. • More meaningful findings names, understandable by computers • Enables the system to infer knowledge Semantic Reading – Intelligent applications
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Siemens AG 2013 All rights reserved. Page 25 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Anatomy aware literature references Image understanding enables to display literature references matching the underlying anatomy, e.g. the Bosnial classification for renal cysts. Example subjects: • Bosniak renal cyst classification • Fleischner pulmonary nodule management • Couinaud liver segments • Lung segments • Hydronephrosis classification • TNM Semantic lesion progression Related findings retrieved with semantic reasoning Semantic Reading – Intelligent applications
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Siemens AG 2013 All rights reserved. Page 26 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO DISEASE MODELING
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Siemens AG 2013 All rights reserved. Page 27 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO clinical data Medical Images Reports lymph node of head Lingual lymph node mandibular lymph node lymph node lymph node of trunk malar lymph node size modifier shrunken Annotations facial lymph node enlarged MEDICO-Annotation-Ontology WhatweareusingOurextension Use of external knowledge lymphoma hasLeadingSymptom symptom disease is-a is-a enlarged lymph node Disease-Symptom-Ontology DiSy:hasModifier DiSy:locatedIn Inferlikelydiseases Clinical Recommendations Next Examinations From Annotations to Diagnosis
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Siemens AG 2013 All rights reserved. Page 28 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Differential Diagnosis Definitions Probabilities Correlation Source: Herold, Innere Medizin, 2011 Analysis of available Clinical Knowledge
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Siemens AG 2013 All rights reserved. Page 29 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO 5 Diseases Hodgkin-Lymphoma Non-Hodgkin-Lymphoma Correctal Carcinoma Reactive Lymphadenitis 40 Symptoms If possible with definition and probabilities Encoding with RadLex or SNOMED CT 10 Dummy Patients Information about Leading Symptoms Test Data Set provided by experts
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Siemens AG 2013 All rights reserved. Page 30 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Ranking factors: Age, gender, specific incidence Leading symptoms Symptom intensity Reappearing symptoms Relative importance of symptoms Ratio of present and absent symptoms of a disease Towards a Ranking of Likely Diseases in Terms of Precision and Recall Heiner Oberkampf, Sonja Zillner, Bernhard Bauer, Matthias Hammon, Netmed2012. Ranking of Likely Diseases
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Siemens AG 2013 All rights reserved. Page 31 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Ranked symptoms list is provided Recommendations of next examinations Decision Support
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Siemens AG 2013 All rights reserved. Page 32 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO SEMANTIC SEARCH
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Siemens AG 2013 All rights reserved. Page 33 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Medical images can only be searched using: • meta data in so-called DICOM-headers (patient name, acquisition date, imaging modality etc.) • indirectly by searching corresponding radiology reports ‘Content’ of the images can not be used for • quality control • data mining for clinical / epidemiological studies • decision support • workflow improvements • reporting support Semantic Limitations of Today's Hospital IT wrt Medical Images
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Siemens AG 2013 All rights reserved. Page 34 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO
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Siemens AG 2013 All rights reserved. Page 35 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO „Find patients with similar liver lesion, enlarged (pathological) lymph nodes in the thorax, hemoglobin value low and patient age greater than 65“ Resulting patients with similar findings Reference lesion Findings histogram for query refinement Accessing reports and lab values Query terms Integrated Semantic Image Search PET-CT 32% MRI 12% Acquisition methods CHOP14 DXBEAM_C Treatments IMVP16 Germany • DSHNHL2004-2 (FLYER) Phase 3 • DSHNHL2006-1B (ACT-2) Phase 3 • DSHNHL2002-1 (Mega-CHOEP) Phase 3 USA • UCLA-0406049-01 Phase 3 Clinical trials
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Siemens AG 2013 All rights reserved. Page 36 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO „Search for images and texts of patients with thickened wall of intestine and hemoglobin value low“ (cancer?) Including lab values Understanding the anatomy: intestine expands to rectum, colon, sigmoid, cecum, … Full text search is not enough! Integrated Semantic Image Search
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Siemens AG 2013 All rights reserved. Page 37 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Image Retrieval - Results • 111 liver lesion with 6105 pairs annotated according to similarity • Annotated with 5 similarity levels; Leave-One-Out validation MAP: 0.78 nDCG(10): 0.85 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.000.250.500.751.00 Recall Precision search input output Hammon, M.; Dankerl, P.; Costa, M.; Tsymbal, A.; Seifert, S.; Sühling, M.; Uder, M. & Cavallaro, A. (2012), Computer-aided decision support for the characterization of liver lesions in CT scans, in 'Proceedings of the European Congress of Radiology (ECR)‘.
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Siemens AG 2013 All rights reserved. Page 38 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO • transform data into actionable knowledge by image, text and speech understanding • harmonize CAD by using standardized vocabulary (CAD group, SCR, and 3rd party) • improve interoperability by describing content in a standardized (semantic web) way • enable inter-modal semantic navigation, i.e. between image, text and other clinical data • Understanding content is the key for proactive context-sensitive workflow support • organize information semantically and prepare for data analytics (understanding first) • formalize medical knowledge instead of programming • enable reuse of third party knowledge networks / databases (publishers, clinical trials, ICD10, ….) Summary
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Siemens AG 2013 All rights reserved. Page 39 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO Dr. Sascha Seifert H SY TI / Germany Hartmannstr. 16 91052 Erlangen E-mail: saschaseifert@siemens.com Contact Disclaimer: The MEDICO prototypes are under development and not commercially available, and their future availability cannot be ensured. The prototypes should not be used for any patient diagnosis or therapy. MEDICO is not related to the commercial hospital information system Medico. Acknowledgements: The MEDICO project is supported in part by the THESEUS program, which is funded by the German Federal Ministry of Economics and Technology under the grant number 01MQ07016. The responsibility for this demonstration lies with the authors.
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Siemens AG 2013 All rights reserved. Page 40 Dr. Sascha Seifert / Healthcare, Imaging & Therapy, SYNGO syngo. It’s all about you.
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