Ole Jakob Elle

About

Cerebral palsy (CP), one of the most common causes of physical disability in childhood, affects movement, balance and posture due to atypical brain development or damage to motor control areas. Clinical gait analysis (CGA) is essential for the diagnosis, treatment planning and management of CP, and motion capture systems are an important tool for assessing movement patterns. Markerless motion capture systems using computer vision and machine learning algorithms offer several advantages over traditional marker-based methods, including improved patient comfort, mobility and reduced set-up time. However, these systems face challenges, such as pose ambiguities in multi-person images and occlusions, which can reduce the accuracy of kinematic data in clinical applications.In this paper, we prese

Selected publications

• Scientific articles and book chapters
• Kupcikevicius, Ignas; Boretto, Luca; Grünbeck, Inger Annett; Kumar, Rahul Prasanna; Nainamalai, Varatharajan & Akhavi, Seyed Mohammadmehdi Sadat [Show all 8 contributors for this article] (2026). AI-Enabled Vessels Segmentation Model for Real-Time Laparoscopic Ultrasound Imaging. Proceedings of Machine Learning Research (PMLR). 307. Full text in Research Archive
• Sajadi, Seyedmohammadreza; Tariverdi, Abbas; Brun, Henrik; Elle, Ole Jakob & Mathiassen, Kim (2026). Robotic transesophageal echocardiography: system design and deep learning-based kinematic modeling. Frontiers in Robotics and AI. 12. doi: 10.3389/frobt.2025.1705142. Full text in Research Archive
• Frostelid, Vetle Christoffer; Wajdan, Ali; Villegas-Martinez, Manuel; Hammersbøen, Lars Egil Reine; Espinoza, Andreas & Grymyr, Ole-Johannes Holm Nielsen [Show all 9 contributors for this article] (2025). Continuous and Autonomous Monitoring of Changes in Left Ventricular dP/dt<inf>max</inf>Using an Epicardial Accelerometer. Annals of Biomedical Engineering. ISSN 0090-6964. 53(11), p. 2783–2794. doi: 10.1007/s10439-025-03828-6. Full text in Research Archive
• Homlong, Eirik Gromholt; Qadir, Hemin Ali; Kumar, Rahul Prasanna; Elle, Ole Jakob & Wiig, Ola (2025). Addressing Occlusions and Pose Challenges in Clinical Gait Analysis: A Robust 3D-to-2D Motion Pipeline. IEEE Engineering in Medicine and Biology Society. Conference Proceedings. ISSN 1557-170X. 47. doi: 10.1109/EMBC58623.2025.11254493. Full text in Research Archive Show summary Cerebral palsy (CP), one of the most common causes of physical disability in childhood, affects movement, balance and posture due to atypical brain development or damage to motor control areas. Clinical gait analysis (CGA) is essential for the diagnosis, treatment planning and management of CP, and motion capture systems are an important tool for assessing movement patterns. Markerless motion capture systems using computer vision and machine learning algorithms offer several advantages over traditional marker-based methods, including improved patient comfort, mobility and reduced set-up time. However, these systems face challenges, such as pose ambiguities in multi-person images and occlusions, which can reduce the accuracy of kinematic data in clinical applications.In this paper, we present a workflow that addresses these challenges by leveraging machine learning and computer vision techniques to improve the reliability and accuracy of markerless motion capture in clinical gait analysis (CGA). Specifically, our approach utilizes the projection of three-dimensional (3D) onto two-dimensional (2D) points to map the original 3D motion capture points onto the video plane and track the subject using distance-based metrics. Additionally, we integrate multiple data modalities to improve robustness. The proposed workflow enables detailed comparisons between markerless and marker-based systems. By improving the performance and applicability of markerless motion capture, this study contributes to a more accessible and effective clinical assessment of children with CP.
• Grünbeck, Inger Annett; Kumar, Rahul Prasanna; Teatini, Andrea; Elle, Ole Jakob & Wiig, Ola (2025). 4D mixed reality tool for orthopaedic surgery - a feasibility study. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization. ISSN 2168-1163. 13(1). doi: 10.1080/21681163.2025.2589161. Full text in Research Archive Show summary Accurately diagnosing joint conditions and bone deformations relying on 2D imaging remains challenging. This paper examined the feasibility of a new workflow for real-time motion visualisation of a patient’s hip joint, using our novel 4D mixed reality tool providing a non-invasive ‘X-ray vision’ experience. A feasibility study was conducted to evaluate the workflow in a clinical context, asking surgeons to locate pain-causing bone deformations during patient examinations, using X-ray, CT, and our tool. Questionnaires with a Likert scale ranging from ‘Strongly Disagree’ to ‘Strongly Agree’ confirmed the clinical feasibility of our workflow. Surgeons highly rated our tool’s visualisation capabilities and assigned the highest marks when asked about their confidence in pinpointing the deformities’ locations (‘Strongly Agree’): X-ray (0%), CT (25%), presented tool (100%). We believe our visualisation tool will become an asset in advancing orthopaedic diagnostics in the future, contributing to improved patient care and surgical outcomes.
• Nainamalai, Varatharajan; Jenssen, Håvard; Boretto, Luca; Luthra, Nikhil André Kumar; Espinoza, Andreas & Pelanis, Egidijus [Show all 11 contributors for this article] (2025). Morphological changes on the human liver during minimally invasive surgery: Implications for image-guided interventions and surgical navigation. Surgical Endoscopy. ISSN 0930-2794. doi: 10.1007/s00464-025-12392-y. Full text in Research Archive
• d’Albenzio, Gabriella; Meng, Ruoyan; Aghayan, Davit; Pelanis, Egidijus; Sakinis, Tomas & Solberg, Ole Vegard [Show all 11 contributors for this article] (2025). Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins. Computer Assisted Surgery. 30(1). doi: 10.1080/24699322.2025.2580307. Full text in Research Archive Show summary Purpose: Couinaud’s liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver segmentation method based on detailed classification of hepatic and portal veins to improve anatomical adherence and clinical relevance. Methods: Our proposed method involves two key stages: (1) surgeons annotate vascular endpoints on 3D models of hepatic and portal veins, from which vessel centerlines are computed; and (2) liver segments are calculated by assigning voxel labels based on proximity to these vascular centerlines. The accuracy and clinical applicability of our Hepatic and Portal Vein-based Classification (HPVC) were compared with conventional Plane-Based Classification (PBC), Portal Vein-Based Classification (PVC), and an automated deep learning method (nnU-Net) using volumetric measurements, Dice similarity scores, and expert evaluations. Results: HPVC demonstrated superior anatomical conformity compared to traditional methods, especially in complex segments like 5 and 8, providing segmentations more reflective of actual vascular territories. Volumetric analysis revealed significant discrepancies among the methods, particularly with nnU-Net generally producing larger segment volumes. HPVC consistently achieved higher surgeon-rated scores in patient-specific anatomical adherence, perfusion region assessment, and accuracy in surgical planning compared to PBC, PVC, and nnU-Net. Conclusion: The presented HPVC method offers substantial improvements in liver segmentation precision, especially relevant for surgical planning in anatomically complex cases. Its integration into clinical workflows via the open-source platform 3D Slicer significantly enhances its accessibility and usability.
• Jenssen, Håvard; Nainamalai, Varatharajan; Pelanis, Egidijus; Kumar, Rahul Prasanna; Abildgaard, Andreas & Kolrud, Finn Kristian [Show all 11 contributors for this article] (2025). Challenges and artificial intelligence solutions for clinically optimal hepatic venous vessel segmentation. Biomedical Signal Processing and Control. ISSN 1746-8094. 106. doi: 10.1016/j.bspc.2025.107822. Full text in Research Archive Show summary Background : Liver vessel identification is crucial for clinical disease assessment and treatment planning, especially concerning local treatment of liver tumors. As artificial intelligence (AI) develops in radiology, opportunities arise to craft models adept at hepatic venous vessel segmentation, opening possibilities for creating patient-specific models of the liver anatomy quickly, despite the diverse features of CT images encountered in clinical settings. Objective: This research evaluates the performance of AI models combined with various pre-processing filters for liver vessel segmentation, emphasizing clinically relevant results. A novel evaluation method was introduced to offer more anatomically accurate assessments, moving beyond traditional metrics like the Dice score. Methods: Using open-source and proprietary datasets, we implemented residual UNet and Dense UNet in combination with smoothness and vesselness filters. We used a clinical evaluation approach focused on major and minor liver vessels, thereby underscoring the precision of AI outcomes. Results: The Dense UNet model with a specific pre-processing filter produced an average Dice score of 0.8144 in our internal dataset. For the public test dataset, the score was 0.7859. Both scores were higher than those not using pre-processing filters, 0.8052 and 0.7765. Clinical assessments showed 85% of AI predictions accurately identified all wanted vessel structures, though segmentation beyond the vessel borders did occur in half the predictions. Conclusion: This study highlights the effectiveness of AI in liver vessel segmentation, with the Dense UNet model combined with pre-processing filters showing high Dice scores and clinical accuracy.