Computergestützte Diagnostik und Therapie – Seminar

Ziel ist die Vertiefung der Vorlesungsinhalte in einem Themenbereich der computergestützten Diagnostik oder Therapie. Studierende bearbeiten einzeln oder in Zweiergruppen ein solches Thema. Um ihre Klausurzulassung zu erhalten, müssen Studierende verschiedene Modalitäten erfüllen. Die Inhalte der Veranstaltung richten sich an Bachelorstudierende der Fächer Medizintechnik und Computervisualistik und Masterstudierende aus Medical Systems Engineering.

• • Bewertungsvorlage Vorträge
  • Kurzanleitung zum Zitieren
  • Klausur WS 2018/2019
  • LaTeX Template für Ausarbeitungen

(1) Blutflussdiagnostik

• Köhler B 2018: Visual and quantitative analysis of great arteries’ blood flow jets in cardiac 4D PC-MRI data.
• Meuschke M 2016: Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta.
• Allen BD 2013: Incorporating time-resolved three-dimensional phase contrast (4D flow) MRI in clinical workflow: initial experiences at a large tertiary care medical center.
• Hope MD 2013: Cardiothoracic Magnetic Resonance Flow Imaging.

(2) Diagnostik von Lungenerkrankungen

• Kuhnigk JM 2008: Quantitative Analysis of Lung Morphology and Function in Computed Tomographic Images.
• Ross JC 2009: Lung Extraction, Lobe Segmentation and Hierarchical Region Assessment for Quantitative Analysis on High Resolution Computed Tomography Images.
• Ärzteblatt 2015: Lungenkrebsscreening: Hoffnungsvolle Erwartungen und Zweifel an der Anwendbarkeit.

(3) Epidemiologie

• Alemzadeh S 2017: Visual Subpopulation Discovery and Validation in Cohort Study Data.
• Klemm P 2015: 3D Regression Heat Map Analysis of Population Study Data.
• Marathe M 2013: Computational Epidemiology.
• Zhang Z 2012: Interactive Visual Patient Cohort Analysis.

(4) Perfusionsdiagnostik

• Behrens S 2007: Computer assistance for MR based diagnosis of breast cancer: Present and future challenges.
• Nowinski WL 2008: Stroke Suite: Cad Systems for Acute Ischemic Stroke, Hemorrhagic Stroke, and Stroke in ER.
• Glaßer S 2009: A Visual Analytics Approach to Diagnosis of Breast DCE-MRI Data.
• Glaßer S 2013: Can we Distinguish Between Benign and Malignant Breast Tumors in DCE-MRI by Studying a Tumor’s Most Suspect Region Only?

(5) Visualisierung und Analyse zerebraler Aneurysmen

• Glaßer S 2014: Combined Visualization of Wall Thickness and Wall Shear Stress for the Evaluation of Aneurysms.
• Lawonn K 2015: Occlusion-free Blood Flow Animation with Wall Thickness Visualization.
• Meuschke M 2018: Classification of Blood Flow Patterns in Cerebral Aneurysms.
• Meuschke M 2018: Visual Analysis of Aneurysm Data using Statistical Graphics.

(6) Diagnoseunterstützung durch Maschinelles Lernen

• Xie B 2012: Real-Time Sleep Apnea Detection by Classifier Combination
• Siddiqui ZF 2015: Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI)
• Krause J. et al. 2016: Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
• Segovia 2019: Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

(7) Deep Learning

• Litjens G 2016: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
• Cheng JZ 2016: Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans
• Engelhardt S 2018: Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries.
• Chen 2022: White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

(8) Radiomics

• Bannach et al. - Visual Analytics for Radiomics: Combining Medical Imaging with Patient Data for Clinical Research (2017)
• Gillies et al. - Radiomics: Images Are More than Pictures, They Are Data (2016)
• Yip and Aerts - Applications and Limitations of Radiomics (2016)
• Kontos 2019: Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment

(9) Computergestützte Diagnostik und Therapie in der Orthopädie (Implantatplanung)

• Dick C 2008: Computational Steering for Patient-Specific Implant Planning in Orthopedics.
• Dick C 2009: Stress Tensor Field Visualization for Implant Planning in Orthopedics.
• Krekel PR 2006: Interactive simulation and comparative visualization of the bone-determined range of motion of the human shoulder.
• Pöppe 2021: The “springform” technique in cranioplasty: custom-made 3D‑printed templates for intraoperative modeling of polymethylmethacrylate cranial implants.

(10) Fluoreszenz-basierte operative Unterstützung am Beispiel von Gehirntumoren (Gliom)

• Hefti M 2010: Fluorescence-Guided Surgery for Malignant Glioma: A Review on Aminolevulinic Acid Induced Protoporphyrin IX Photodynamic Diagnostic in Brain Tumors.
• Li 2013: Intraoperative Fluorescence-Guided Resection of High-Grade Gliomas: A Comparison of the Present Techniques and Evolution of Future Strategies.

(11) Segmentierung

• Heckel F 2013: Sketch-Based Editing Tools for Tumour Segmentation in 3D Medical Images.
• Wang L 2012: A Minimally-interactive Method to Segment Enlarged Lymph Nodes in 3D Thoracic CT Images Using a Rotatable Spiral-Scanning Technique.

(12) Strahlentherapie

• Jaffray D 2008: Radiation Oncology.
• Raidou R 2020: VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy
• Schlachter et al. 2019 – State-of-the-Art Report – Visual Computing in Radiation Therapy Planning

(13) Wirbelsäulenchirurgie

• Hintz K 2008: Konzeption und Implementierung eines Trainingssystems fur die Anatomie und Chirurgie der Wirbelsäule.
• Metz LN 2008: Computer-Assisted Surgical Planning and Image-Guided Surgical Navigation in Refractory Adult Scoliosis Surgery.
• Wolf et al. 2023 – How different augmented reality visualizations for drilling affect trajectory deviation, visual attention, and user experience

(14) Therapie mittels Smart Wearables

• Bus, SA 2016: Innovations in plantar pressure and foot temperature measurements in diabetes.
• Niemann, U 2016: Learning Pressure Patterns for Patients with Diabetic Foot Syndrome.
• Deschamps, 2016: Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.
• Banaee, H 2013: Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges.

(15) Virtual Reality für die Interventionsplanung

• Bashkanov et al. - VR Multi-User Conference Room for Surgery Planning (2019)
• Detmer et al. - Virtual and Augmented Reality Systems for Renal Interventions: A Systematic Review (2017)
• Huber et al. - Artificial Versus Video-Based Immersive Virtual Surroundings: Analysis of Performance and User's Preference (2018)
• Chheang et al. - Collaborative Virtual Reality for Laparoscopic Liver Surgery Training (2019)

(16) Nadelbasierte Thermalablationen für Tumorerkrankungen

• Rieder C 2010: Visual Support for Interactive Post-Interventional Assessment of Radiofrequency Ablation Therapy.
• Chu et al. - Thermal Ablation of Tumours: Biological Mechanisms and Advances in Therapy (2014)
• Ahmed et al. - Principles of and Advances in Percutaneous Ablation (2011)
• Longfei, 2022: Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors.

(17) Visual Analytics für Prostatakarzinome

• Bernard et al. - A Visual Interactive System for Prostate Cancer Cohort Analysis (2015)
• Bernard et al. - A Visual Active Learning System for the Assessment of Patient Well-Being in Prostate Cancer research (2015)
• Bernard et al. - Using Dashboard Networks to Visualize Multiple Patient Histories a Design Study on Post Operative Prostate Cancer (2019)

(18) Digitale Pathologie

• Lotz et al. - Zooming In: High Resolution 3D Reconstruction of Differently Stained Histological Whole Slide Images (2014)
• Bejnordi et al. - Stain Specific Standardization of Whole Slide Histopathological Images (2016)
• Corvo et al. - PathoVA: A Visual Analytics Tool for Pathology Diagnosis and Reporting (2017)
• Corvo et al. - Visual Analytics in Digital Pathology Challenges and Opportunities (2019)

(19) Brain Flattening

• Kreiser et al. - A Survey of Flattening-Based Medical Visualization Techniques (2018)
• Pons et al. - Area Preserving Cortex Unfolding (2004)
• Ju et al. - Cortical surface flattening using least square conformal mapping with minimal metric distortion (2004)

(20) Tracking und Visualisierung von Gehirnfasern

• Jeurissen et al. - Diffusion MRI fiber tractography of the brain (2019)
• Carnevale et al. - Brain MRI fiber-tracking reveals white matter alterations in hypertensive patients without damage at conventional neuroimaging (2018)
• Barbieri · et al. - Atlas-based fiber reconstruction from diffusion tensor MRI data (2012)

(21) Vessel Flattening

• Kreiser et al. - A Survey of Flattening-Based Medical Visualization Techniques (2018)
• Mistelbauer et al. - Vessel Visualization using Curvicircular Feature Aggregation (2013)
• Eulzer, 2020: Temporal Views of Flattened Mitral Valve Geometries.
• Eulzer, 2022: Vessel Maps: A Survey of Map-Like Visualizations of the Cardiovascular System.

(22) Medizinische Illustration und Operationsplanung

• Tietjen et al. - Combining Silhouettes, Surface, and Volume Rendering for Surgery Education and Planning (2005)
• Lawonn et al. - A Survey on Multimodal Medical Data Visualization (2018)
• Haidacher et al. - Importance-Driven Rendering in Interventional Imaging (2007)

(23) Medizinische Animationen

• Preim et al. - A Survey of Medical Animations (2020)
• Lundstroem et al. - Uncertainty visualization in medical volume rendering using probabilistic animation (2007)

(24) Virtual Reality für die Medizinische Ausbildung

• Hsieh et al. - Preliminary Study of VR and AR Applications in Medical and Healthcare Education (2017)
• Saalfeld et al. - Student and Teacher Meet in a Shared Virtual Reality: A one-on-one Tutoring System for Anatomy Education (2020)
• Schott, 2023: CardioGenesis4D: Interactive Morphological Transitions of Embryonic Heart Development in a Virtual Learning Environment.