Eigil Samset

• Windecker, Stephan; Fraser, Alan G; Szymanski, Piotr; Gilard, Martine; Lüscher, Thomas F & Abid, Leila [Vis alle 27 forfattere av denne artikkelen] (2025). Priorities for medical device regulatory approval: a report from the European Society of Cardiology Cardiovascular Round Table. European Heart Journal. ISSN 0195-668X. 46(16), s. 1469–1479. doi: 10.1093/eurheartj/ehaf069. Fulltekst i vitenarkiv Vis sammendrag

  Abstract The European Union (EU) Medical Device Regulation increased regulatory scrutiny to improve the safety and performance of new medical devices. An equally important goal is providing timely access to innovative devices to benefit patient care. The European Society of Cardiology strongly advocates for the evolution of the Medical Device Regulation system to facilitate priority access for innovative devices for unmet needs and orphan cardiovascular (CV) medical devices in EU countries. Although device approval is currently executed by Notified Bodies in the EU, it will be advantageous in the mid-term to consider a single EU regulatory agency for devices. In the short term, steps can be taken to transform the current system into a more efficient, predictable, cost-effective, and user-friendly service. Key strategies include the following: enhancing predictability of the approval process through use of early scientific advice from regulators; establishing unique regulatory pathways for CV orphan, paediatric, and innovative devices; promoting more efficient (re)certification of essential legacy CV devices; improving transparency of sponsor interactions with Notified Bodies; expanding the roles of the Expert Panels to assist in the approval of CV devices; promoting global regulatory harmonization, considering streamlined authorization of CV medical technologies across selected jurisdictions; developing an efficient system to monitor device safety; and ensuring funding for data collection platforms. Some strategies that could help include considering a pilot programme for joint approval processes of selected devices in partnership with other regions (i.e. US Food and Drug Administration); developing priority pathways for accelerated access to innovative or orphan devices; and increasing recognition of the importance of early feasibility studies in the EU.

• Andreassen, Børge Solli; Thomas, Sarina; Solberg, Anne H Schistad; Samset, Eigil & Volgyes, David (2024). Introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography. I Gomez, Alberto; Khanal, Bishesh; King, Andrew & Namburete, Ana (Red.), Simplifying Medical Ultrasound: 5th International Workshop, ASMUS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. Springer Nature Switzerland. ISSN 9783031736476. s. 122–131. doi: 10.1007/978-3-031-73647-6_12. Fulltekst i vitenarkiv Vis sammendrag

  The morphology of the mitral annulus plays an important role in diagnosing and treating mitral valve disorders. Automated segmentation has the promise to be time-saving and improve consistency in clinical practice. In the past years, segmentation has been dominated by methods based on deep learning. Deep learning-based segmentation methods have shown good results, but their consistency and robustness are still subjects of active research. In this work, we introduce a method that combines Graph Convolutional Networks with a 3D CNN model to integrate an anatomical shape template for the predictions. Our method leverages the feature extraction capability of CNN models to provide input features to the graph neural networks. The proposed method leverages strengths from a shape model approach with the strengths of deep learning. Further, we propose loss functions for the CNN designed to guide the graph model training. The CNN was trained with transfer learning, using a limited number of labeled transesophageal echocardiography volumes to adapt to the mitral annulus segmentation task. When comparing the segmentation of the mitral annulus achieved by the proposed method with the test set annotations, the method showed a high degree of accuracy, achieving a curve-to-curve error of 2.00±0.81 mm and a relative perimeter error of 4.42±3.33%. Our results show that the proposed method is a promising new approach for introducing anatomical template structures in medical segmentation tasks.

• Windecker, Stephan; Gilard, Martine; Achenbach, Stephan; Cribier, Alain; Delgado, Victoria & Deych, Nataliya [Vis alle 22 forfattere av denne artikkelen] (2024). Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table. European Heart Journal. ISSN 0195-668X. 45(13), s. 1104–1115. doi: 10.1093/eurheartj/ehae069. Fulltekst i vitenarkiv
• Rogstadkjernet, Magnus; Zha, Sigurd Zijun; Klæboe, Lars Gunnar; Larsen, Camilla Kjellstad; Aalen, John & Scheirlynck, Esther [Vis alle 14 forfattere av denne artikkelen] (2024). A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers. BMC Medical Imaging. 24(1). doi: 10.1186/s12880-024-01470-7. Fulltekst i vitenarkiv Vis sammendrag

  Background Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regions of interest (ROIs) is labor intensive and may influence assessment of strain values. Purpose We hypothesized that a deep learning (DL) model, trained on clinical echocardiographic exams, can be combined with a readily available echocardiographic analysis software, to automate strain calculation with comparable fidelity to trained cardiologists. Methods Data consisted of still frame echocardiographic images with cardiologist-defined ROIs from 672 clinical echocardiographic exams from a university hospital outpatient clinic. Exams included patients with ischemic heart disease, heart failure, valvular disease, and conduction abnormalities, and some healthy subjects. An EfficientNetB1-based architecture was employed, and different techniques and properties including data set size, data quality, augmentations, and transfer learning were evaluated. DL predicted ROIs were reintroduced into commercially available echocardiographic analysis software to automatically calculate strain values. Results DL-automated strain calculations had an average absolute difference of 0.75 (95% CI 0.58–0.92) for global longitudinal strain (GLS), and 1.16 (95% CI 1.03–1.29) for single-projection longitudinal strain (LS), compared to operators. A Bland–Altman plot revealed no obvious bias, though there were fewer outliers in the lower average LS ranges. Techniques and data properties yielded no significant increase/decrease in performance. Conclusion The study demonstrates that DL-assisted, automated strain measurements are feasible, and provide results within interobserver variation. Employing DL in echocardiographic analyses could further facilitate adoption of STE parameters in clinical practice and research, and improve reproducibility.

• Zha, Sigurd Zijun; Rogstadkjernet, Magnus; Klæboe, Lars Gunnar; Skulstad, Helge; Singstad, Bjørn-Jostein & Gilbert, Andrew David [Vis alle 9 forfattere av denne artikkelen] (2023). Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography. Cardiovascular Ultrasound. 21(1), s. 1–11. doi: 10.1186/s12947-023-00317-5. Fulltekst i vitenarkiv Vis sammendrag

  Background Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists. Methods Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1–6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model. Results The mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90–1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6–2.7) %, which was comparable to the clinicians for the test set. Conclusion DL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization. Graphical Abstract

• Andreassen, Børge Solli; Volgyes, David; Samset, Eigil & Solberg, Anne H Schistad (2022). Mitral Annulus Segmentation and Anatomical Orientation Detection in TEE Images Using Periodic 3D CNN. IEEE Access. 10, s. 51472–51486. doi: 10.1109/ACCESS.2022.3174059. Fulltekst i vitenarkiv Vis sammendrag

  Segmentation of the mitral annulus is often an important step in cardiac examinations. We propose a robust 3D method for predicting the anatomical orientation and segmentation of the mitral annulus in 3D transesophageal echocardiography. The method takes advantage of the circular anatomy of the annulus by utilizing cylinder coordinate samples and a 3D convolutional neural network with circular convolutions. Furthermore, the paper proposes new landmark detection loss functions based on the earth mover’s distance. The method’s effectiveness was demonstrated by training a HighRes3dNet model and evaluating its performance on a separate test set consisting of 135 frames from 19 examinations. The obtained coordinate prediction error was 1.96± 1.62 mm, and the anatomical orientation prediction error was 9.7° ± 15.8°. The robust and fully automatic mitral annulus segmentation and orientation prediction provided by the method can ease the workload of clinicians and provide time savings in clinics.

• Bølviken, Håkon Strand; Veronesi, Federico & Samset, Eigil (2022). Simultaneous segmentation of all four chambers in cardiac ultrasound images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization. ISSN 2168-1163. doi: 10.1080/21681163.2022.2073913. Fulltekst i vitenarkiv Vis sammendrag

  Echocardiographic analysis of cardiac chamber volumes throughout the heart cycle is important to assess left- and right-heart function. We have developed and validated a method to simultaneously segment all four heart chambers in 3D echocardiography cine loops. We described the heart using several Doo Sabin models, one for each chamber and one for the pericardium. The model was fitted to 3D echo images using edge detection and a Kalman filter. The average signed error in estimating volume across all chambers was −1.0 ±± 12.3 ml. These results show that the model slightly underestimates the volumes. Average signed error for the ejection fraction was −3.2 ± 7.4 pts, with the highest error being the right atrium. This all-chamber automatic segmentation method provides high accuracy and may have utility in understanding chamber interactions.

• Andreassen, Børge Solli; Veronesi, Federico; Gerard, Olivier; Solberg, Anne H Schistad & Samset, Eigil (2020). Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194. 24(4), s. 994–1003. doi: 10.1109/JBHI.2019.2959430. Fulltekst i vitenarkiv Vis sammendrag

  3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and post-processing to the test set data gave a mean error of 2.0 mm - with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

• Gilbert, Andrew David; Holden, Marit; Eikvil, Line; Rakhmail, Mariia; Babic, Aleksandar & Aase, Svein Arne [Vis alle 8 forfattere av denne artikkelen] (2020). User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194. doi: 10.1109/JBHI.2020.3029392. Fulltekst i vitenarkiv Vis sammendrag

  Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give insight into myocardial motion and blood flow providing clinicians with parameters for diagnostic decision making. Many of these measurements are performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline. However, full automation is not yet available because the user must manually select which measurement should be performed on each image. In this work, we develop a pipeline based on convolutional neural networks (CNNs) to automatically classify the measurement type from cardiac Doppler scans. We show how the multi-modal information in each spectral Doppler recording can be combined using a meta parameter post-processing mapping scheme and heatmaps to encode coordinate locations. Additionally, we experiment with several architectures to examine the tradeoff between accuracy, speed, and memory usage for resource-constrained environments. Finally, we propose a confidence metric using the values in the last fully connected layer of the network and show that our confidence metric can prevent many misclassifications. Our algorithm enables a fully automatic pipeline from acquisition to Doppler spectrum measurements. We achieve 96% accuracy on a test set drawn from separate clinical sites, indicating that the proposed method is suitable for clinical adoption.

• Bølviken, Håkon Strand; Bersvendsen, Jørn; Orderud, Fredrik; Snare, Sten Roar; Brekke, Pål Haugar & Samset, Eigil (2020). Two methods for modifed Doo-Sabin modeling of nonsmooth surfaces - Applied to right ventricle modeling. Journal of Medical Imaging. ISSN 2329-4302. 7(6). doi: 10.1117/1.JMI.7.6.067001. Fulltekst i vitenarkiv Vis sammendrag

  Purpose: In recent years, there has been increased clinical interest in the right ventricle (RV) of the heart. RV dysfunction is an important prognostic marker for several cardiac diseases. Accurate modeling of the RV shape is important for estimating the performance. We have created computationally effective models that allow for accurate estimation of the RV shape. Approach: Previous approaches to cardiac shape modeling, including modeling the RV geometry, has used Doo–Sabin surfaces. Doo–Sabin surfaces allow effective computation and adapt to smooth, organic surfaces. However, they struggle with modeling sharp corners or ridges without many control nodes. We modified the Doo–Sabin surface to allow for sharpness using weighting of vertices and edges instead. This was done in two different ways. For validation, we compared the standard Doo–Sabin versus the sharp Doo–Sabin models in modeling the RV shape of 16 cardiac ultrasound images, against a ground truth manually drawn by a cardiologist. A Kalman filter fitted the models to the ultrasound images, and the difference between the volume of the model and the ground truth was measured. Results: The two modified Doo–Sabin models both outperformed the standard Doo–Sabin model in modeling the RV. On average, the regular Doo–Sabin had an 8-ml error in volume, whereas the sharp models had 7- and 6-ml error, respectively. Conclusions: Compared with the standard Doo–Sabin, the modified Doo–Sabin models can adapt to a larger variety of surfaces while still being compact models. They were more accurate on modeling the RV shape and could have uses elsewhere.

• Salles, Sebastien; Kvåle, Kaja Frøysaa; Lervik, Lars Christian Naterstad; Støylen, Asbjørn; Løvstakken, Lasse & Samset, Eigil [Vis alle 7 forfattere av denne artikkelen] (2020). Detection of Tissue Fibrosis using Natural Mechanical Wave Velocity Estimation: Feasibility Study. Ultrasound in Medicine and Biology. ISSN 0301-5629. 46(9), s. 2481–2492. doi: 10.1016/j.ultrasmedbio.2020.04.022. Fulltekst i vitenarkiv
• Babic, Aleksandar; Odland, Hans Henrik; Lyseggen, Erik; Holm, Torbjørn; Ross, Stian Balnagown & Hopp, Einar [Vis alle 11 forfattere av denne artikkelen] (2019). An image fusion tool for echo‐guided left ventricular lead placement in cardiac resynchronization therapy: Performance and workflow integration analysis. Echocardiography. ISSN 0742-2822. 36(10), s. 1834–1845. doi: 10.1111/echo.14483. Fulltekst i vitenarkiv Vis sammendrag

  Background The response rate to cardiac resynchronization therapy (CRT) may be improved if echocardiographic‐derived parameters are used to guide the left ventricular (LV) lead deployment. Tools to visually integrate deformation imaging and fluoroscopy to take advantage of the combined information are lacking. Methods An image fusion tool for echo‐guided LV lead placement in CRT was developed. A personalized average 3D cardiac model aided visualization of patient‐specific LV function in fluoroscopy. A set of coronary venography‐derived landmarks facilitated registration of the 3D model with fluoroscopy into a single multimodality image. The fusion was both performed and analyzed retrospectively in 30 cases. Baseline time‐to‐peak values from echocardiography speckle‐tracking radial strain traces were color‐coded onto the fused LV. LV segments with suspected scar tissue were excluded by cardiac magnetic resonance imaging. The postoperative augmented image was used to investigate: (a) registration accuracy and (b) agreement between LV pacing lead location, echo‐defined target segments, and CRT response. Results Registration time (264 ± 25 seconds) and accuracy (4.3 ± 2.3 mm) were found clinically acceptable. A good agreement between pacing location and echo‐suggested segments was found in 20 (out of 21) CRT responders. Perioperative integration of the proposed workflow was successfully tested in 2 patients. No additional radiation, compared with the existing workflow, was required. Conclusions The fusion tool facilitates understanding of the spatial relationship between the coronary veins and the LV function and may help targeted LV lead delivery.

• Kvåle, Kaja Frøysaa; Salles, Sebastien; Brekke, Pål Haugar; Remme, Espen W.; Aalen, John & Edvardsen, Thor [Vis alle 7 forfattere av denne artikkelen] (2019). Comparison of two methods for mechanical activation detection using high frame rate ultrasound imaging. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. 2019-October, s. 1410–1413. doi: 10.1109/ULTSYM.2019.8925759. Fulltekst i vitenarkiv
• Kvåle, Kaja Frøysaa; Bersvendsen, Jørn; Remme, Espen W.; Salles, Sebastien; Aalen, John & Brekke, Pål Haugar [Vis alle 8 forfattere av denne artikkelen] (2019). Detection of regional mechanical activation of the left ventricular myocardium using high frame rate ultrasound imaging. IEEE Transactions on Medical Imaging. ISSN 0278-0062. 38:8682078(11), s. 2665–2675. doi: 10.1109/TMI.2019.2909358. Fulltekst i vitenarkiv
• Kvåle, Kaja Frøysaa; Salles, Sebastien; Brekke, Pål Haugar; Edvardsen, Thor & Samset, Eigil (2019). Velocity estimation of naturally occurring mechanical waves in the left ventricle in healthy persons. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. 2019-October, s. 1425–1428. doi: 10.1109/ULTSYM.2019.8926213. Fulltekst i vitenarkiv
• Gilbert, Andrew David; Holden, Marit; Eikvil, Line; Aase, Svein Arne; Samset, Eigil & Mcleod, Kristin (2019). Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11798 LNCS, s. 29–37. doi: 10.1007/978-3-030-32875-7_4. Fulltekst i vitenarkiv Vis sammendrag

  Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to automate 2DE LV measurements. Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations. To improve the network performance we use anatomically meaningful heatmaps as labels and train with a multi-component loss function. Our network achieves 13.4%, 6%, and 10.8% mean percent error on intraventricular septum (IVS), LV internal dimension (LVID), and LV posterior wall (LVPW) measurements respectively. The design outperforms other networks and matches or approaches intra-analyser expert error.

• Hubert, Arnaud; Rolle, Virginie Le; Leclercq, Christophe; Galli, Elena; Samset, Eigil & Casset, Cyrille [Vis alle 9 forfattere av denne artikkelen] (2018). Estimation of myocardial work from pressure?strain loops analysis: an experimental evaluation. European Heart Journal-Cardiovascular Imaging. ISSN 2047-2404. 19(12), s. 1372–1379. doi: 10.1093/ehjci/jey024. Fulltekst i vitenarkiv
• Galli, Elena; Leclercq, Christophe; Fournet, Maxime; Hubert, Arnaud; Bernard, Anne & Smiseth, Otto A. [Vis alle 10 forfattere av denne artikkelen] (2018). Value of myocardial work estimation in the prediction of response to cardiac resynchronization therapy. Journal of the American Society of Echocardiography. ISSN 0894-7317. 31(2), s. 220–230. doi: 10.1016/j.echo.2017.10.009. Fulltekst i vitenarkiv
• Kozolovski, Pavel; Rodriguez-Molares, Alfonso; Tangen, Thor Andreas; Kristoffersen, Kjell; Torp, Hans & Gérard, Olivier [Vis alle 7 forfattere av denne artikkelen] (2018). Adaptive color gain for vena contracta quantification in valvular regurgitation. Ultrasound in Medicine and Biology. ISSN 0301-5629. 44(8), s. 1770–1777. doi: 10.1016/j.ultrasmedbio.2018.04.002. Fulltekst i vitenarkiv
• Galli, Elena; Leclercq, Christophe; Hubert, Arnaud; Bernard, Anne; Smiseth, Otto A. & Mabo, Philippe [Vis alle 9 forfattere av denne artikkelen] (2018). Role ofmyocardial constructive work in the identification of responders to CRT. European Heart Journal-Cardiovascular Imaging. ISSN 2047-2404. 19(9), s. 1010–1018. doi: 10.1093/ehjci/jex191. Fulltekst i vitenarkiv
• Nordenfur, Tim; Babic, Aleksandar; Bulatovic, Ivana; Giesecke, Anders; Gunyeli, Elif & Ripsweden, Jonaz [Vis alle 9 forfattere av denne artikkelen] (2018). Method comparison for cardiac image registration of coronary computed tomography angiography and 3-D echocardiography. Journal of Medical Imaging. ISSN 2329-4302. 5_014001, s. 1–10. doi: 10.1117/1.JMI.5.1.014001. Fulltekst i vitenarkiv
• Santos, Pedro; Løvstakken, Lasse; Samset, Eigil & D'Hooge, Jan (2017). Volumetric imaging of fast mechanical waves in the heart using a clinical ultrasound system. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. doi: 10.1109/ULTSYM.2017.8091558. Fulltekst i vitenarkiv
• Kozlowski, Pawel; Urheim, Stig & Samset, Eigil (2017). Evaluation of a multi-view autostereoscopic real-time 3D ultrasound system for minimally invasive cardiac surgery guidance. IEEE International Symposium on Biomedical Imaging. ISSN 1945-7928. s. 604–607. doi: 10.1109/ISBI.2017.7950593. Fulltekst i vitenarkiv
• Almeida, Nuno; Papachristidis, Alexandros; Pearson, Peter; Sarvari, Sebastian Imre; Engvall, Jan & Edvardsen, Thor [Vis alle 10 forfattere av denne artikkelen] (2017). Left atrial volumetric assessment using a novel automated framework for 3D echocardiography: a multi-centre analysis. European Heart Journal-Cardiovascular Imaging. ISSN 2047-2404. 18(9), s. 1008–1015. doi: 10.1093/ehjci/jew166. Fulltekst i vitenarkiv
• Babic, Alexandear; Odland, Hans Henrik; Gérard, Olivier & Samset, Eigil (2017). Left-ventricle to coronary venous tree 3D fusion for cardiac resynchronization therapy applications. IEEE International Symposium on Biomedical Imaging. ISSN 1945-7928. s. 164–168. doi: 10.1109/ISBI.2017.7950493. Fulltekst i vitenarkiv
• Kozlowski, Pawel; Bandaru, Raja Sekhar; D'Hooge, Jan & Samset, Eigil (2017). Real-time catheter localization and visualization using three-dimensional echocardiography. Progress in Biomedical Optics and Imaging. ISSN 1605-7422. 10135, s. 1–6. doi: 10.1117/12.2253835. Fulltekst i vitenarkiv
• Bersvendsen, Jørn; Orderud, Fredrik; Lie, Øyvind Haugen; Massey, Richard J; Fosså, Kristian & Estépar, Raúl San José [Vis alle 8 forfattere av denne artikkelen] (2017). Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces. Journal of Medical Imaging. ISSN 2329-4302. 4:024005(2), s. 1–xx. doi: 10.1117/1.JMI.4.2.024005. Fulltekst i vitenarkiv
• Santos, Pedro; Haugen, Geir Ultveit; Løvstakken, Lasse; Samset, Eigil & D'Hooge, Jan (2016). High frame rate 3D tissue velocity imaging using sub-aperture beamforming: A pilot study in vivo. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. 2016-November. doi: 10.1109/ULTSYM.2016.7728385. Fulltekst i vitenarkiv
• Danudibroto, Adriyana; Bersvendsen, Jørn; Mirea, Oana; Gèrard, Olivier; D'Hooge, Jan & Samset, Eigil (2016). Image-based temporal alignment of echocardiographic sequences. Progress in Biomedical Optics and Imaging. ISSN 1605-7422. 9790. doi: 10.1117/12.2216192. Fulltekst i vitenarkiv
• Almeida, Nuno; Friboulet, Denis; Sarvari, Sebastian Imre; Bernard, Olivier; Barbosa, Daniel & Samset, Eigil [Vis alle 7 forfattere av denne artikkelen] (2016). Left-Atrial Segmentation from 3-D Ultrasound Using B-Spline Explicit Active Surfaces with Scale Uncoupling. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. ISSN 0885-3010. 63(2), s. 212–221. doi: 10.1109/TUFFC.2015.2507638. Fulltekst i vitenarkiv
• Bersvendsen, Jørn; Toews, Matthew; Danudibroto, Adriyana; Wells, William M; Urheim, Stig & Estépar, Raúl San José [Vis alle 7 forfattere av denne artikkelen] (2016). Robust spatio-Temporal registration of 4D cardiac ultrasound sequences. Progress in Biomedical Optics and Imaging. ISSN 1605-7422. 9790. doi: 10.1117/12.2217005. Fulltekst i vitenarkiv
• Danudibroto, Adriyana; Bersvendsen, Jørn; Gèrard, Olivier; Mirea, Oana; D'hooge, Jan R M & Samset, Eigil (2016). Spatiotemporal registration of multiple three-dimensional echocardiographic recordings for enhanced field of view imaging. Journal of Medical Imaging. ISSN 2329-4302. 3(3). doi: 10.1117/1.JMI.3.3.037001. Fulltekst i vitenarkiv
• Almeida, Nuno; Sarvari, Sebastian Imre; Orderud, Fredrik; Gèrard, Olivier; D'hooge, Jan R M & Samset, Eigil (2016). Automatic left-Atrial segmentation from cardiac 3D ultrasound: A dual-chamber model-based approach. Progress in Biomedical Optics and Imaging. ISSN 1605-7422. 9790:97900D. doi: 10.1117/12.2216666. Fulltekst i vitenarkiv
• Santos, Pedro; Haugen, Geir Ultveit; Løvstakken, Lasse; Samset, Eigil & D'hooge, Jan R M (2016). Diverging Wave Volumetric Imaging Using Subaperture Beamforming. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. ISSN 0885-3010. 63(12), s. 2114–2124. doi: 10.1109/TUFFC.2016.2616172. Fulltekst i vitenarkiv
• Bersvendsen, Jørn; Orderud, Fredrik; Massey, Richard; Fosså, Kristian; Gèrard, Olivier & Urheim, Stig [Vis alle 7 forfattere av denne artikkelen] (2016). Automated Segmentation of the Right Ventricle in 3D Echocardiography: A Kalman Filter State Estimation Approach. IEEE Transactions on Medical Imaging. ISSN 0278-0062. 35(1), s. 42–51. doi: 10.1109/TMI.2015.2453551. Fulltekst i vitenarkiv
• Babic, Alexandar; Odland, Hans Henrik; Gèrard, Olivier & Samset, Eigil (2015). Parametric ultrasound and fluoroscopy image fusion for guidance of left ventricle lead placement in cardiac resynchronization therapy. Journal of Medical Imaging. ISSN 2329-4302. 2(2). doi: 10.1117/1.JMI.2.2.025001. Fulltekst i vitenarkiv
• Santos, Pedro; Tong, Ling; Ortega, Alejandra; Løvstakken, Lasse; Samset, Eigil & D'hooge, Jan R M (2015). Acoustic output of multi-line transmit beamforming for fast cardiac imaging: A simulation study. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. ISSN 0885-3010. 62(7), s. 1320–1330. doi: 10.1109/TUFFC.2015.006996. Fulltekst i vitenarkiv
• Bandaru, Raja Sekhar; Sørnes, Anders; D'Hooge, Jan & Samset, Eigil (2014). 2D localization of specular reflections using ultrasound. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. s. 2209–2212. doi: 10.1109/ULTSYM.2014.0550. Fulltekst i vitenarkiv
• Røislien, Jo & Samset, Eigil (2014). A non-parametric permutation method for assessing agreement for distance matrix observations. Statistics in Medicine. ISSN 0277-6715. 33(2), s. 319–329. doi: 10.1002/sim.5927. Fulltekst i vitenarkiv
• Bersvendsen, Jørn; Beitnes, Jan Otto; Urheim, Stig; Aakhus, Svend & Samset, Eigil (2014). Automatic measurement of aortic annulus diameter in 3-dimensional transoesophageal echocardiography. BMC Medical Imaging. 14(31). doi: 10.1186/1471-2342-14-31. Fulltekst i vitenarkiv Vis sammendrag

  Background Transcatheter aortic valve implantation involves percutaneously implanting a biomechanical aortic valve to treat severe aortic stenosis. In order to select a proper device, precise sizing of the aortic valve annulus must be completed. Methods In this paper, we describe a fully automatic segmentation method to measure the aortic annulus diameter in patients with aortic calcification, operating on 3-dimensional transesophageal echocardiographic images. The method is based on state estimation of a subdivision surface representation of the left ventricular outflow tract and aortic root. The state estimation is solved by an extended Kalman filter driven by edge detections normal to the subdivision surface. Results The method was validated on echocardiographic recordings of 16 patients. Comparison against two manual measurements showed agreements (mean ±SD) of -0.3±1.6 and -0.2±2.3 mm for perimeter-derived diameters, compared to an interobserver agreement of -0.1±2.1 mm. Conclusions With this study, we demonstrated the feasibility of an efficient and fully automatic measurement of the aortic annulus in patients with aortic disease. The algorithm robustly measured the aortic annulus diameter, providing measurements indistinguishable from those done by cardiologists.

• Santos, Pedro; Tong, Ling; Ortega, Alejandra; Løvstakken, Lasse; Samset, Eigil & D'hooge, Jan R M (2014). Safety of Multi-Line Transmit beam forming for fast cardiac imaging - A simulation study. IEEE International Ultrasonics Symposium Proceedings. ISSN 1051-0117. s. 1199–1202. doi: 10.1109/ULTSYM.2014.0295. Fulltekst i vitenarkiv
• Almeida, Nuno; Barbosa, Daniel; Heyde, Brecht; Mada, Razvan Olimpiu; Friboulet, Denis & Bernard, Olivier [Vis alle 8 forfattere av denne artikkelen] (2014). Semi-automatic left-atrial segmentation from volumetric ultrasound using B-spline explicit active surfaces. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. s. 612–615. doi: 10.1109/ULTSYM.2014.0150. Fulltekst i vitenarkiv
• Melvær, Eivind Lyche; Mørken, Knut Martin & Samset, Eigil (2012). A motion constrained cross-wire phantom for tracked 2D ultrasound calibration. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 7(4), s. 611–620. doi: 10.1007/s11548-011-0661-6. Fulltekst i vitenarkiv
• Morvan, Tangui; Reimers, Martin & Samset, Eigil (2012). Efficient Image-Based Proximity Queries with Object-Space Precision. Computer Graphics Forum. ISSN 0167-7055. 31(1), s. 62–74. doi: 10.1111/j.1467-8659.2011.02084.x. Fulltekst i vitenarkiv
• Freudenthal, Adinda; Stüdeli, Thomas; Lamata, Pablo & Samset, Eigil (2011). Collaborative co-design of emerging multi-technologies for surgery. Journal of Biomedical Informatics. ISSN 1532-0464. 44(2), s. 198–215. doi: 10.1016/j.jbi.2010.11.006. Fulltekst i vitenarkiv
• Parmar, Ashis Jalote; Badke-Schaub, Petra; Wajid, Ali & Samset, Eigil (2010). Cognitive processes as integrative component for developing expert decision-making systems: A workflow centered framework. Journal of Biomedical Informatics. ISSN 1532-0464. 43(1), s. 60–74. doi: 10.1016/j.jbi.2009.07.001. Fulltekst i vitenarkiv
• Furtado, Hugo; Stüdeli, Thomas; Sette, Mauro; Morita, Terumasa; Trunk, Primoz & Freudenthal, Adinda [Vis alle 9 forfattere av denne artikkelen] (2010). Endoclamp Balloon Visualization and Automatic Placement System. Heart Surgery Forum. ISSN 1098-3511. 13(4), s. E205–E211. doi: 10.1532/HSF98.20091169. Fulltekst i vitenarkiv
• Petter, Risholm; Melvær, Eivind Lyche; Mørken, Knut Martin & Samset, Eigil (2009). Intra-operative adaptive FEM-based registration accommodating tissue resection. I Josien, Pluim & Dawant, Benoit (Red.), Medical Imaging 2009: Image Processing (Proceedings Volume). SPIE - The International Society for Optics and Photonics. ISSN 9780819475107. Fulltekst i vitenarkiv
• Milko, Sergiy; Melvær, Eivind Lyche; Samset, Eigil & Kadir, Timor (2009). A Novel Method for Registration of US/MR of the Liver Based on the Analysis of US Dynamics. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 5761, s. 771–778. doi: 10.1007/978-3-642-04268-3_95. Fulltekst i vitenarkiv
• Milko, Sergiy; Melvær, Eivind Lyche; Samset, Eigil & Kadir, Timor (2009). Evaluation of bivariate correlation ratio similarity metric for rigid registration of US/MR images of the liver. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 4(2), s. 147–155. Fulltekst i vitenarkiv
• Morvan, Tangui; Martinsen, M; Reimers, Martin; Samset, Eigil & Elle, Ole Jakob (2009). Collision detection and untangling for surgical robotic manipulators. International Journal of Medical Robotics and Computer Assisted Surgery. ISSN 1478-5951. 5(3), s. 233–242. doi: 10.1002/rcs.247. Fulltekst i vitenarkiv
• Risholm, Petter & Samset, Eigil (2009). Haptic guided 3-D deformable image registration. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 4(3), s. 215–223. Fulltekst i vitenarkiv
• Risholm, Petter; Samset, Eigil; Talos, Ion-Florin & Wells, William (2009). A Non-rigid Registration Framework That Accommodates Resection and Retraction. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 5636, s. 447–458. Fulltekst i vitenarkiv
• Morvan, Tangui; Reimers, Martin & Samset, Eigil (2008). High Performance GPU-based Proximity Queries using Distance Fields. Computer Graphics Forum. ISSN 0167-7055. 27(8), s. 2040–2052. doi: 10.1111/j.1467-8659.2008.01183.x. Fulltekst i vitenarkiv
• Cornella, Jordi; Elle, Ole Jakob; Ali, Wajid & Samset, Eigil (2008). Interoperative Navigation of an Optically Tracked Surgical Robot. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11(Pt2), s. 587–594. Fulltekst i vitenarkiv
• Milko, Sergiy; Samset, Eigil & Kadir, Timor (2008). Segmentation of the liver in ultrasound: a dynamic texture approach. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 3(1-2), s. 143–150. Fulltekst i vitenarkiv
• Folkesson, Jenny; Samset, Eigil; Kwong, R.Y. & Westin, Carl-Fredric (2008). Unifying Statistical Classification and Geodesic Active Regions for Segmentation of Cardiac MRI. IEEE transactions on information technology in biomedicine. ISSN 1089-7771. 12(3), s. 328–334. Fulltekst i vitenarkiv
• Samset, Eigil; Schmalstieg, Dieter; Sloten, Jos Vander; Freudenthal, Adinda; Declerck, Jerome & Casciaro, Sergio [Vis alle 8 forfattere av denne artikkelen] (2008). Augmented reality in surgical procedures. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 6806. doi: 10.1117/12.784155. Fulltekst i vitenarkiv
• Risholm, Petter; Narum, E; Elle, Ole Jakob & Samset, Eigil (2007). An inexpensive and portable system for improving EM tracking accuracy. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2. Fulltekst i vitenarkiv
• Balasingham, Ilangko; Ihlen, Halfdan; Leister, Wolfgang; Røe, Per & Samset, Eigil (2007). Communication of medical images, text, and messages in inter-enterprise systems: A case study in Norway. IEEE transactions on information technology in biomedicine. ISSN 1089-7771. 11(1), s. 7–13. doi: 10.1109/TITB.2006.879597. Fulltekst i vitenarkiv Vis sammendrag

  There is an increasing demand to discuss diagnostic images and reports of difficult cases with experienced staff. A possible solution besides physically transporting patients and material is to use high-speed communication networks to transfer images and reports electronically. With the web application PACSflow we have developed a solution to transfer images, reports, and messages as a single package in a one-step procedure. The PACSflow is an interoperable and standard compliant web-based application, which gives clinicians a user-friendly interface for their work on a daily basis. The solution assumes that the diagnostic images are compatible with the digital imaging and communications in medicine (DICOM) format. The Department of Cardiology at the Rikshospitalet University Hospital in Oslo, Norway, and the Department of Internal Medicine at the Sørlandet Sykehus in Arendal, Norway, are making clinical use of the system. Initial tests indicate that use of PACSflow has reduced the time required to prepare and transfer data by a factor of 3.

• Larsen, Siri Øyen; Mørken, Knut Martin & Samset, Eigil (2007). Segmentation of the frozen region in MR images during cryo ablation in the liver. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2, s. 479–479. Fulltekst i vitenarkiv
• Risholm, Petter; Nærum, Edvard; Elle, Ole Jakob & Samset, Eigil (2007). An inexpensive and portable system for improving electromagnetic tracking accuracy. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2, s. 181–181. Fulltekst i vitenarkiv
• Hans, Arne; Ellis, Randy & Samset, Eigil (2007). Out-of-plane CT guidance for needle placement using an open source navigation framework. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2, s. 467–467. Fulltekst i vitenarkiv
• Spiczak, Jochen Von; Samset, Eigil; DiMaio, Simon; Reitmayr, G.; Schmalstieg, Dieter & Burghart, C. [Vis alle 7 forfattere av denne artikkelen] (2007). Device connectivity for image-guided medical applications. Studies in Health Technology and Informatics. ISSN 0926-9630. 125, s. 482–484. Fulltekst i vitenarkiv
• DiMaio, Simon; Samset, Eigil; Fischer, G; Iordachita, I.; Fichtinger, G. & Jolesz, Ferenc [Vis alle 7 forfattere av denne artikkelen] (2007). Dynamic MRI scan plane control for passive tracking of instruments and devices. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 4792(10), s. 50–58. Fulltekst i vitenarkiv
• Vosburgh, Kirby; Stylopoulus, Nicholas; Estepar, Raul; Ellis, Randy; Samset, Eigil & Thompson, C (2007). EUS with CT improves efficency and structure identification over conventional EUS. Gastrointestinal Endoscopy. ISSN 0016-5107. 65(6), s. 866–870. Fulltekst i vitenarkiv
• Parmar, Ashis Jalote; Goossens, Richard; Ridder, Huib De; Pattynama, Peter & Samset, Eigil (2007). A surgeon centered framework towards analyzing the surgical workflow. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2, s. 154–154. Fulltekst i vitenarkiv
• Samset, Eigil & DiMaio, Simon (2007). Hybrid Tracking: A new trend in Image-Guided Therapy. Berichte aus der Informatik. ISSN 0945-0807. Fulltekst i vitenarkiv
• Spiczak, Jochen Von; DiMaio, Simon; Reitmayr, G.; Schmalstieg, Dieter & Samset, Eigil (2007). Multi-Modal Event Streams for Virtual Reality. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 6504. doi: 10.1117/12.706079. Fulltekst i vitenarkiv
• Samset, Eigil; Hans, Arne; DiMaio, Simon & Jolesz, Ferenc (2007). A dynamic and extensible workflow-oriented software framework for image-guided therapy. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410. 2(3-4), s. 221–229. Fulltekst i vitenarkiv
• Estepar, Raul; Stylopoulus, Nicholas; Ellis, Randy; Samset, Eigil; Westin, Carl-Fredric & Thompson, Christopher R. [Vis alle 7 forfattere av denne artikkelen] (2007). Towards scarless surgery: An endoscopic ultrasound navigation system for transgastric access procedures. Computer Aided Surgery. ISSN 1092-9088. 12(6), s. 311–324. Fulltekst i vitenarkiv
• Hoge, W. Scott; Chu, Renxin; Jolesz, Ferenc & Samset, Eigil (2007). Fast Regularized Parallel Imaging in an MR Image-Guided Therapy Application. Asilomar Conference on Signals, Systems and Computers. Conference Record. ISSN 1058-6393. 1, s. 1869–1873. Fulltekst i vitenarkiv
• Parmar, Ashis Jalote; Pattynama, Peter; Goossens, Richard; Freudenthal, Adinda; Samset, Eigil & Ridder, Huib De (2007). Bridging the gap: A user centered design approach towards developing technological solutions. I Casciaro, Sergio & Samset, Eigil (Red.), Novel Technologies for Minimally Invasive Therapies. Lupiensis Biomedical Publications. ISSN 9788890288005. s. 93–101. Fulltekst i vitenarkiv
• Morvan, Tangui; Martinsen, Magnar; Parmar, Ashis Jalote; Reimers, Martin; Samset, Eigil & Elle, Ole Jakob (2007). A Generic Monitoring System for Surgical Robotic Manipulators. I Casciaro, Sergio & Samset, Eigil (Red.), Novel Technologies for Minimally Invasive Therapies. Lupiensis Biomedical Publications. ISSN 9788890288005. s. 158–165. Fulltekst i vitenarkiv
• Freudenthal, Adinda; Pattynama, Peter; Samset, Eigil; Parmar, Ashis Jalote; Gersak, Borut & Casciaro, Sergio (2007). Technology assessment as guidance in product development for - real time - radiology based information for the surgeon. I Casciaro, Sergio & Samset, Eigil (Red.), Novel Technologies for Minimally Invasive Therapies. Lupiensis Biomedical Publications. ISSN 9788890288005. s. 180–188. Fulltekst i vitenarkiv
• Parmar, Ashis Jalote; Pattynama, Peter; Goossens, Richard; Freudenthal, Adinda; Samset, Eigil & Ridder, Huib De (2007). Surgical workflow analysis for identifying user requirements for surgical information systems. I Pikaar, R. N.; Koningsveld, E. A. P. & Settels, P. J. M. (Red.), Meeting Diversity in Ergonomics. Elsevier. ISSN 9780080453736. s. 229–242. Fulltekst i vitenarkiv
• Jolesz, Ferenc & Samset, Eigil (2007). Clinical Applications of Interventional and Intraoperative MRI. I F., Reiser Maximilian; Semmler, Wolfhard & Hricak, Hedvig (Red.), Magnetic Resonance Tomography. Springer. ISSN 9783540293545. s. 1277–1290. Fulltekst i vitenarkiv

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• Casciaro, Sergio & Samset, Eigil (2007). Novel Technologies for Minimally Invasive Therapies. Lupiensis Biomedical Publications. ISBN 9788890288005. 190 s. Fulltekst i vitenarkiv

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• Bølviken, Håkon Strand; Bersvendsen, Jørn; Orderud, Fredrik; Snare, Sten Roar; Brekke, Pål & Samset, Eigil (2020). Modified Doo-Sabin Modeling of the Right Heart Ventricle. Fulltekst i vitenarkiv
• Samset, Eigil (2009). Visualisering av store petroleumsreservoarmodeller. Fulltekst i vitenarkiv
• Samset, Eigil (2009). Visualisering av store petroleumsreservoarmodeller. Fulltekst i vitenarkiv
• Risholm, Petter; Samset, Eigil; Talos, Ion-Florin & Wells, William (2009). A Non-rigid Registration Framework That Accommodates Resection and Retraction. Fulltekst i vitenarkiv
• Milko, Sergiy; Melvær, Eivind Lyche; Samset, Eigil & Kadir, Timor (2009). A Novel Method for Registration of US/MR of the Liver Based on the Analysis of US Dynamics. Fulltekst i vitenarkiv
• Cornella, Jordi; Elle, Ole Jakob; Ali, Wajid & Samset, Eigil (2008). Interoperative Navigation of an Optically Tracked Surgical Robot. Fulltekst i vitenarkiv
• Mørken, Knut Martin & Samset, Eigil (2008). Datagrafikk og matematikk gir nye muligheter på operasjonsstuen. Fulltekst i vitenarkiv
• Samset, Eigil (2008). Augmented reality in surgical procedures. Fulltekst i vitenarkiv
• Samset, Eigil (2008). I3 Program at Brigham. Fulltekst i vitenarkiv
• Stüdeli, Thomas; Freudenthal, Adinda & Samset, Eigil (2007). Assessing Quality of Computer Systems and User Interfaces of Future Medical Workplaces - Augmented Reality for Minimally Invasive Therapy. Fulltekst i vitenarkiv
• Samset, Eigil; Hoge, W. Scott & Jolesz, Ferenc (2007). Interactive MR imaging using parallel acquisition and parallel reconstruction. Fulltekst i vitenarkiv
• Hans, Arne; Ellis, Randy & Samset, Eigil (2007). Out-of-plane CT guidance for needle placement using an open source navigation framework. Fulltekst i vitenarkiv
• Risholm, Petter; Nærum, Edvard; Elle, Ole Jakob & Samset, Eigil (2007). An inexpensive and portable system for improving electromagnetic tracking accuracy. Fulltekst i vitenarkiv
• Parmar, Ashis Jalote; Goossens, Richard; Ridder, Huib De; Pattynama, Peter & Samset, Eigil (2007). A surgeon centered framework towards analyzing the surgical workflow. Fulltekst i vitenarkiv
• Mørken, Knut Martin; Samset, Eigil & Larsen, Siri Øyen (2007). Segmentation of the frozen region in MR images during cryo ablation in the liverduring cryo ablation in the liver. Fulltekst i vitenarkiv
• Mørken, Knut Martin & Samset, Eigil (2007). MATMED: Mathematical and computational methods for co-registering multi-modal medical images. Fulltekst i vitenarkiv
• Mørken, Knut Martin & Samset, Eigil (2007). Mathematical and computational challenges in some applications of medical imaging. Fulltekst i vitenarkiv
• Mørken, Knut Martin; Bruvoll, Solveig & Samset, Eigil (2007). Medical image registration. Fulltekst i vitenarkiv
• Morvan, Tangui; Martinsen, M.; Parmar, Ashis Jalote; Reimers, Martin; Elle, Ole Jakob & Samset, Eigil (2006). A generic monitoring system for surgical robotic manipulators. Fulltekst i vitenarkiv
• Samset, Eigil; Hans, Arne; Spiczak, Jochen Von; DiMaio, Simon; Ellis, Randy & Hata, Nobuhiko [Vis alle 7 forfattere av denne artikkelen] (2006). The SIGN: A dynamic and extensible software framework for Image-Guided Therapy. Fulltekst i vitenarkiv
• Andreassen, Børge Solli; Samset, Eigil; Solberg, Anne H Schistad; Thomas, Sarina & Volgyes, David (2025). Deep Learning for Mitral Annulus Delineation in 3D Transesophageal Echocardiography. Universitetet i Oslo. Fulltekst i vitenarkiv Vis sammendrag

  Valvular heart disease is a significant health concern, particularly in aging populations, with mitral valve disease being among the most common. Three-dimensional transesophageal echocardiography (TEE) provides high-quality imaging for assessing the mitral valve. Delineating the mitral annulus in 3D images is an important step for accurate quantitative morphological assessment of the valve. However, manual analysis is time-consuming and prone to interobserver variability. Deep learning is a class of machine learning methods that has led to significant progress across an extensive range of applications. In medical imaging, it has enabled improvements across a wide array of tasks, including classification, segmentation, and landmark detection. In this thesis, deep learning is applied to automate the delineation of the mitral annulus from 3D TEE images. The thesis presents a progression of methods across three papers, each building on the last, gradually incorporating more anatomical context and spatial structure. The first paper applies a 2D model with post-processing to enforce annular continuity. The second paper introduces a periodic 3D model using cylindrical coordinates, enabling direct segmentation and prediction of anatomical orientation. The third paper integrates anatomical constraints through a graph-based model to improve the robustness and consistency of the predicted annulus. The methods were evaluated on annotated 3D TEE datasets, showing accurate and consistent delineation of the mitral annulus across multiple subjects. In addition to the specific application, the thesis proposes a novel loss function and coordinate decoding strategy for deep learning with potential applicability in landmark detection tasks, both in medical and natural images. These Developments support the potential for integration into practical workflows, which could assist clinicians in assessing mitral valves while contributing to automation in cardiac imaging.

• Bersvendsen, Jørn; Samset, Eigil; Mørken, Knut Martin & Reimers, Martin (2016). Segmentation of cardiac structures in 3-dimensional echocardiography. Universitetet i Oslo. Fulltekst i vitenarkiv Vis sammendrag

  Cardiac ultrasound is a key component in modern cardiology, and is widely used to assess and quantify the heart’s anatomy and function. In the past two decades, real-time 3-dimensional ultrasound has emerged as a promising modality, allowing a more accurate appreciation of the heart’s complex geometry, compared to the more conventional 2-dimensional ultrasound. However, the huge increases in information within volumetric images demand accurate and efficient methods for automatically analyzing and quantifying the heart. One of the fundamental problems in the processing of cardiac images is segmentation; the process of extracting a geometric model from the image, describing the shape and motion of an anatomical structure. A framework has previously been proposed for solving this problem in 3D ultrasound images, in which the segmentation is expressed as a state-estimation problem and solved with a Kalman filter. This framework is generic, and allows for incorporating different surface representations, image measurements and prior information on shape and motion, and it has been applied to the left ventricle. The main goal of this thesis has been to apply the existing Kalman filter segmentation framework to different anatomical structures, in particular the aortic root and right ventricle, and to develop a method for biventricular segmentation. Four main contributions have been made. Firstly, a method providing fully automatic quantification of the aortic valve size from 3D ultrasound images has been developed and its feasibility demonstrated. This is a key measurement to be made prior to procedures on diseased valves, and performing manual measurements is time-consuming and subject to inter-observer variation. Secondly, two methods have been developed for automated segmentation of the Right Ventricle (RV). Historically, the importance of the RV has been underestimated. Although the RV’s role in cardiovascular diseases has been more widely recognized in recent years, few methods for segmentation of the RV have been proposed. Because the RV has a more complex and asymmetrical shape compared to the left ventricle, the existing segmentation framework is not immediately applicable to the RV. Therefore, the method was extended with a geometric model learned from statistical analysis on a set of manual segmentations, as well as prior information on the appearance of the ultrasound images. Thirdly, one of the two RV methods uses a novel geometric surface representation to simultaneously segment the endocardial and epicardial borders of both ventricles. The surface is parameterized such that myocardial volume conservation is used within the Kalman filter segmentation process, which leads to a much more complete model of the heart and allows one to study the interaction between the ventricles. Finally, a robust and fully automatic method for spatio-temporal fusion of two ultrasound images has been developed. This is important as it is unrealistic to capture both ventricles in the same imaging sector. Furthermore, the method is able to perform the spatial registration with no assumption on the relationship between the recordings.

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