About

Nikolaos Papanikolopoulos is a professor at the University of Minnesota, holding the titles of McKnight Presidential Endowed Professor, Distinguished McKnight University Professor, and Director of Graduate Studies for Robotics at the Minnesota Robotics Institute. He joined the Department of Computer Science & Engineering in 1992 and was promoted to full professor in 2001. His research interests include robotics, sensors for transportation applications, computer vision, and control systems. He is the founder of ReconRobotics Inc. and serves as a consultant for numerous companies including Toyota, Canon, Intel, and Best Buy. Papanikolopoulos has contributed to the creation of the Department of Electrical and Computer Engineering at the University of Cyprus and has been recognized with several awards, including the IEEE Fellow and the McKnight Presidential Endowed Professorship. His work encompasses developing robotic systems, monitoring human activity, and addressing societal challenges through technological innovation.

Research topics

• Artificial Intelligence
• Medicine
• Internal medicine
• Machine Learning
• Computer Science
• Radiology
• Pathology
• Family medicine
• Surgery

Selected publications

• Environment-Behaviour Inquiry and Children's Mental Health

  2026-01-12

  book-chapterSenior author

  Knowledge of how environmental parameters intersect with daily living is limited for children with mental health disorders. Additional challenges are partly tied to studies mostly drawing from quantitative measures, which further restrict the understanding of the place-making practices of these children. This chapter builds on a 2015 study that used video recordings to quantitatively unravel how children with obsessive-compulsive disorder relate to everyday interior elements, including a table and a sink. Through an observation lens, the 2023 pilot study sheds light on how videos can cultivate empathy and uncover behaviours that could remain hidden through conventional methods. A broader understanding of potential forms of over- and under-engagement with the built environment challenges how mental health is understood and studied.

  PublisherDOI

• MP02-12 AGE DISCREPANCY: A NOVEL PREDICTOR OF FRAILTY IN KIDNEY TUMOR PATIENTS

  The Journal of Urology · 2025-04-08

  article
  PublisherDOI

• BOSfM: A View Planning Framework for Optimal 3D Reconstruction of Agricultural Scenes

  ArXiv.org · 2025-09-28

  preprintOpen accessSenior author

  Active vision (AV) has been in the spotlight of robotics research due to its emergence in numerous applications including agricultural tasks such as precision crop monitoring and autonomous harvesting to list a few. A major AV problem that gained popularity is the 3D reconstruction of targeted environments using 2D images from diverse viewpoints. While collecting and processing a large number of arbitrarily captured 2D images can be arduous in many practical scenarios, a more efficient solution involves optimizing the placement of available cameras in 3D space to capture fewer, yet more informative, images that provide sufficient visual information for effective reconstruction of the environment of interest. This process termed as view planning (VP), can be markedly challenged (i) by noise emerging in the location of the cameras and/or in the extracted images, and (ii) by the need to generalize well in other unknown similar agricultural environments without need for re-optimizing or re-training. To cope with these challenges, the present work presents a novel VP framework that considers a reconstruction quality-based optimization formulation that relies on the notion of `structure-from-motion' to reconstruct the 3D structure of the sought environment from the selected 2D images. With no analytic expression of the optimization function and with costly function evaluations, a Bayesian optimization approach is proposed to efficiently carry out the VP process using only a few function evaluations, while accounting for different noise cases. Numerical tests on both simulated and real agricultural settings signify the benefits of the advocated VP approach in efficiently estimating the optimal camera placement to accurately reconstruct 3D environments of interest, and generalize well on similar unknown environments.

  PublisherOA PDFDOI

• AGE DISCREPANCY: A NOVEL PREDICTOR OF FRAILTY IN KIDNEY TUMOR PATIENTS

  Urologic Oncology Seminars and Original Investigations · 2025-02-27

  article
  PublisherDOI

• A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy

  Urology · 2025-02-04 · 4 citations

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  PublisherDOI

• From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation

  ArXiv.org · 2025-02-04 · 1 citations

  preprintOpen accessSenior author

  This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real time through the VAE decoder. The proposed method shows promising results in teleoperating the manipulator, enabling the generation of novel manipulator configurations from human features that were not present during training.

  PublisherOA PDFDOI

• Ground-Density Clustering for Approximate Agricultural Field Segmentation

  2024-10-14

  articleSenior author

  Instance and semantic segmentation form the backbone of robotic perception and are crucial to many tasks. While most research in the area focuses on improving segmentation quality metrics, there are plenty of applications where approximate methods are adequate as long as they are fast, especially in applications with large amounts of data like precision agriculture. In order to apply the recent successes of machine learning and computer vision on a large scale using robotics, efficient and general algorithms must be designed to intelligently split point clouds into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we capitalize on a similarity between the current state-of-the-art for roughly segmenting corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a novel algorithm, Ground-Density Quickshift++, with the goal of producing a general and scalable field segmentation algorithm that segments individual plants and their stems. This algorithm produces quantitatively better results than the current state-of-the-art on both plant separation and stem segmentation while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithm should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

  PublisherDOI

• AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients

  arXiv (Cornell University) · 2024-06-29

  preprintOpen access

  Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans, as a potential indicator of frailty and postoperative risk in kidney cancer patients. This retrospective study of 599 patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found that a higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors. This suggests that AI Age Discrepancy may provide valuable insights into patient frailty and could thus inform clinical decision-making in kidney cancer treatment.

  PublisherOA PDFDOI

• Fully Automated Versions of Clinically Validated Nephrometry Scores Demonstrate Superior Predictive Utility versus Human Scores

  British Journal of Urology · 2024-02-11 · 6 citations

  articleOpen access

  OBJECTIVE: To automate the generation of three validated nephrometry scoring systems on preoperative computerised tomography (CT) scans by developing artificial intelligence (AI)-based image processing methods. Subsequently, we aimed to evaluate the ability of these scores to predict meaningful pathological and perioperative outcomes. PATIENTS AND METHODS: A total of 300 patients with preoperative CT with early arterial contrast phase were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer. A deep neural network approach was used to automatically segment kidneys and tumours, and then geometric algorithms were used to measure the components of the concordance index (C-Index), Preoperative Aspects and Dimensions Used for an Anatomical classification of renal tumours (PADUA), and tumour contact surface area (CSA) nephrometry scores. Human scores were independently calculated by medical personnel blinded to the AI scores. AI and human score agreement was assessed using linear regression and predictive abilities for meaningful outcomes were assessed using logistic regression and receiver operating characteristic curve analyses. RESULTS: The median (interquartile range) age was 60 (51-68) years, and 40% were female. The median tumour size was 4.2 cm and 91.3% had malignant tumours. In all, 27% of the tumours were high stage, 37% high grade, and 63% of the patients underwent partial nephrectomy. There was significant agreement between human and AI scores on linear regression analyses (R ranged from 0.574 to 0.828, all P < 0.001). The AI-generated scores were equivalent or superior to human-generated scores for all examined outcomes including high-grade histology, high-stage tumour, indolent tumour, pathological tumour necrosis, and radical nephrectomy (vs partial nephrectomy) surgical approach. CONCLUSIONS: Fully automated AI-generated C-Index, PADUA, and tumour CSA nephrometry scores are similar to human-generated scores and predict a wide variety of meaningful outcomes. Once validated, our results suggest that AI-generated nephrometry scores could be delivered automatically from a preoperative CT scan to a clinician and patient at the point of care to aid in decision making.

  PublisherOA PDFDOI

• Efficient MedSAMs: Segment Anything in Medical Images on Laptop

  arXiv (Cornell University) · 2024-12-20

  preprintOpen access

  Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications.

  PublisherOA PDFDOI

Recent grants

• MRI: Development of a Video-Based Robotic Instrument for Behavioral Analysis and Diagnosis of At-Risk Children

  NSF · $1.7M · 2010–2019

• NRI: Collaborative Research: Robotics 2.0 for Disaster Response and Relief Operations

  NSF · $1.0M · 2014–2019

• CPS: TTP Option: Synergy: Collaborative Research: Dynamic Methods of Traffic Control that Impact Quality of Life in Smart Cities

  NSF · $1.2M · 2015–2021

• International Meeting for Computational Methods for Mental Health

  NSF · $7k · 2015–2017

• ITR: Collaborative Research: Multi-Robot Emergency Response

  NSF · $1.8M · 2003–2011

Frequent coauthors

• Vassilios Morellas

  University of Minnesota

  82 shared

• Nicholas Heller

  48 shared

• Christopher J. Weight

  Cleveland Clinic

  37 shared

• Anoop Cherian

  Mitsubishi Electric (United States)

  30 shared

• Resha Tejpaul

  27 shared

• Ajay J. Joshi

  25 shared

• Ravishankar Sivalingam

  25 shared

• Osama Masoud

  23 shared

Labs

• AI for the Changing WorldPI

Awards & honors

• Award for Outstanding Contributions to Postbaccalaureate, Gr…
• McKnight Presidential Endowed Professorship (2016)
• Richard P. Braun Distinguished Service Award (2013)
• IEEE Fellow (2007)
• McKnight Professorships (2007)