Nikolaos Papanikolopoulos

• Ph.D.
• CEO at University of Minnesota

3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. An important approach for this task, known as view planning, is to judiciously place a number of cameras in positions that maximize the visual informati...

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challen...

Objectives: To determine whether we can surpass the traditional R.E.N.A.L. nephrometry score (H-score) prediction ability of pathologic outcomes by creating artificial intelligence(AI)-generated R.E.N.A.L.+ score(AI+score) with continuous rather than ordinal components. We also assessed the AI+ score components' relative importance with respect to...

623 Background: The Centrality index (C-index) score is a validated nephrometry scoring system that requires precise measurements and mathematical calculations of cross sectional imaging. Like other nephrometry scores, its implementation has been slowed by required time investment and interobserver variability. We sought to automate this score on p...

693 Background: The American Urologic Association (AUA) recommends estimation of the postoperative glomerular filtration rate (GFR) in patients with a renal mass to help decide between partial nephrectomy (PN) or radical nephrectomy (RN). If postoperative GFR<45 mL/min/1.73m ² , a PN should be prioritized. Most existing methods to predict postopera...

Purpose: Clinicians rely on imaging features to calculate complexity of renal masses based on validated scoring systems. These scoring methods are labor-intensive and are subjected to interobserver variability. Artificial intelligence has been increasingly utilized by the medical community to solve such issues. However, developing reliable algorith...

Purpose: To automate R.E.N.A.L. nephrometry scoring of preoperative computed tomography (CT) scans and create an artificial intelligence generated score (AI-Score). Subsequently, to evaluate its ability to predict meaningful oncologic and perioperative outcomes as compared to expert human-generated nephrometry scores (H-score.)Materials and Method...

As the quantity and quality of cross-sectional imaging data increase, it is important to be able to make efficient use of the information. Semantic segmentation is an emerging technology that promises to improve the speed, reproducibility, and accuracy of analysis of medical imaging, and to allow visualization methods that were previously impossibl...

In order to apply the recent successes of automated plant phenotyping and machine learning on a large scale, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper we notice a similarity between the current state-o...

Representations in the form of Symmetric Positive Definite (SPD) matrices have been popularized in a variety of visual learning applications due to their demonstrated ability to capture rich second-order statistics of visual data. There exist several similarity measures for comparing SPD matrices with documented benefits. However, selecting an appr...

Neuropsychiatric disorders are highly prevalent conditions with significant individual, societal, and economic impacts. A major challenge in the diagnosis and treatment of these conditions is the lack of sensitive, reliable, objective, quantitative tools to inform diagnosis, and measure symptom severity. Currently available assays rely on self-repo...

Introduction: Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at...

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-q...

Objective: To understand better the public perception and comprehension of medical technology such as artificial intelligence and robotic surgery. Additionally, to identify sensitivity to their use in order to ensure acceptability and quality of counseling. Subjects and methods: A survey was conducted on a convenience sample of visitors to the M...

626 Background: The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was an international competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) and sought to stimulate progress on this automatic segmentation frontier. Growing rates of kidney tumor in...

Objective: To understand better the public perception and comprehension with medical technology such as artificial intelligence and robotic surgery. Additionally, to identify sensitivity to, and comfort with, the use of AI and robotics in medicine a in order to ensure acceptability and quality of counseling and to guide future development. Subjects...

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-q...

Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a huge task-space for benchmark...

Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a huge task-space for benchmark...

This paper presents a topology-based global descriptor that allows for efficient 3D point cloud processing tasks associated with the analysis of shapes. The descriptor is called the Signature of Topologically Persistent Points (STPP). By using persistent homology, STPP is formed by the computation of topological invariants involving the zeroth and...

The use of cross-sectional imaging to inform the decision between partial and radical nephrectomy for kidney tumors has received considerable attention over the last decade. Notably, the R.E.N.A.L., PADUA, and Centrality-Index scoring systems aim to quantify complexity with evaluations based on tumor size, endophycity, and location. We propose the...

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature...

We and others have successfully applied computer vision to diagnosing a variety of malignant neoplasms in histopathologic images. Machine learning being an opaque process, little is known about the basis on which computer vision makes its diagnostic decisions in surgical pathology. Here, we use class saliency maps to determine which parts of the im...

Financial and social elements of modern societies are closely connected to the cultivation of corn. Due to its massive production, deficiencies during the cultivation process directly translate to major financial losses. Since proper surveillance in a large scale is still very challenging, the companies that specialize in optimizing crop yield are...

Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined. Little work has been done to analyze the effect that label errors have on the performance of segmentation methodologies. Here we present a large-scale study of model performance in the presence of varying types and...

Skin cancer is the most common cancer, accounting for over 40% of all cancer cases. The morphological features of skin lesions are an integral component of skin cancer detection and diagnosis. With the rapid progress in the field of image classification, increasing attention has been put towards the Computer Aided Diagnosis of skin lesions based on...

Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined. Little work has been done to analyze the effect that label errors have on the performance of segmentation methodologies. Here we present a large-scale study of model performance in the presence of varying types and...

Air travel is one of the fastest growing modes of transportation, however, the effects of aircraft noise on populations surrounding airports is hindering its growth. In an effort to study and ultimately mitigate the impact that this noise has, many airports continuously monitor the aircraft noise in their surrounding communities. Noise monitoring a...

We present the Signature of Topologically Persistent Points (STPP), a global descriptor that encodes topological invariants of 3D point cloud data. These topological invariants include the zeroth and first homology groups and are computed using persistent homology, a method for finding the features of a topological space at different spatial resolu...

Kidney cancer is projected to be the sixth most common cancer in men and the tenth most common cancer in women in 2018 [PMID29313949]. The morphological and anatomic features of kidneys and renal tumors have been shown to correlate with important patient outcomes [1]. Automatic segmentation with deep learning offers a way to compute these features...

Mental health disorders are a leading cause of disability in North America. An important aspect in treating mental disorders is early intervention, which dramatically increases the probability of positive outcomes; however, early intervention hinges upon knowledge and detection of risk markers for particular disorders. Ideally, the screening of the...

The Vietoris-Rips complex describes the topology of a point cloud. It is used in topological data analysis (TDA) to compute persistent homology, a method for finding topological features at different spatial resolutions. The construction of the Vietoris-Rips complex is the primary bottleneck in TDA due to the necessity of building the complex at di...

Computer Aided Diagnosis (CAD) systems are adopting advancements at the forefront of computer vision and machine learning towards assisting medical experts with providing faster diagnoses. The success of CAD systems heavily relies on the availability of high-quality annotated data. Towards supporting the annotation process among teams of medical ex...

Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence , Jensen-Bregman logdet divergence, etc.; however , their behaviors may be application dependent, raising the nee...

Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their behaviors may be application dependent, raising the need...

Short demonstration video of our ICRA 2017 work "Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications".

The recent activity in the area of autonomous vehicle navigation has initiated a series of reactions that stirred the automobile industry, pushing for the fast commercialization of this technology which, until recently, seemed futuristic. The LiDAR sensor is able to provide a detailed understanding of the environment surrounding the vehicle making...

Objectives: The clinical presentation of pediatric obsessive-compulsive disorder (OCD) is heterogeneous, which is a stumbling block to understanding pathophysiology and to developing new treatments. A major shift in psychiatry, embodied in the Research Domain Criteria (RDoC) initiative of National Institute of Mental Health, recognizes the pitfall...

A 'region' is an important concept in interpreting 3D point cloud data since regions may correspond to objects in a scene. To correctly interpret 3D point cloud data, we need to partition the dataset into regions that correspond to objects or parts of an object. In this paper, we present a region growing approach that combines global (topological)...

Objectives Previous research has examined the influence of the physical environment on the manifestation of mental health conditions such as ASD. This pilot study sought to identify differences in visual preferences in children and adolescents with OCD to begin exploring how visual design may impact OCD and how it may be used to create environments...

This work describes computer vision methods developed for autonomous recharging of a quadrotor from a standard wall outlet. Specifically, this work encompasses two algorithms for detecting and tracking an outlet in a video stream acquired by a low cost quadrotor platform. Two different algorithms were developed for assumptions and requirements asso...

In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extrac...

This chapter introduces the foundation for surveillance and security robots for multiple military and civilian applications. The key environmental domains are mobile robots for ground, aerial, surface water, and underwater applications. Surveillance literally means to watch from above, while surveillance robots are used to monitor the behavior, act...

Determining and detecting risk markers for mental illness remains a labor intensive process, requiring vast amounts of observations by clinical professionals. Motor stereotypies, which are defined as involuntary repetitive motor behaviors, invariant in form, that, to an observer, appear to serve no purpose, are a class of risk markers which are ver...

An object recognition engine needs to extract discriminative features from data representing an object and accurately classify the object to be of practical use in robotics. Furthermore, the classification of the object must be rapidly performed in the presence of a voluminous stream of data. These conditions call for a distributed and scalable arc...

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) def...

Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, Expectation Maximization, etc.) are g...

An approach is proposed for high resolution 3D reconstruction of an object using a Micro Air Vehicle (MAV). A system is described which autonomously captures images and performs a dense 3D reconstruction via structure from motion with no prior knowledge of the environment. Only the MAVs own sensors, the front facing camera and the Inertial Measurem...

Sparse models have proven to be extremely successful in image processing and computer vision. However, a majority of the effort has been focused on sparse representation of vectors and low-rank models for general matrices. The success of sparse modeling, along with popularity of region covariances, has inspired the development of sparse coding appr...

In this paper, we introduce a dictionary learning framework using RGB-D covariance descriptors on point cloud data for performing object classification. Dictionary learning in combination with RGB-D covariance descriptors provides a compact and flexible description of point cloud data. Furthermore, the proposed framework is ideal for updating and s...

The development of large scale marsupial robotic teams has been prohibitive for a number of reasons. The complexity of such systems has been hard to simulate, especially in the case of a 'Many-to-One' relationship between a marsupial robot and the robots it can deploy. Additionally, the construction of physical systems can be expensive to implement...

This paper describes the Microvision, a multipurpose robotic platform developed at the University of Minnesota's Center for Distributed Robotics. The Microvision is equipped with a scanning laser range finder, RGB cameras, depth sensor, IMU sensor, and audio stream capture ability that allow it to navigate and sense the surrounding environment. Cre...

Successful operation of a miniature rotorcraft re-lies on capabilities including automated guidance, trajectory following, and teleoperation; all of which require accurate estimates of the vehicle's body velocities and Euler angles. For larger rotorcraft that operate outdoors, the traditional approach is to combine a highly accurate IMU with GPS me...

In this paper, we present two real-time methods for controlling data transmission in a robotic network that utilizes a remote computing infrastructure. The proposed algorithms use information and communication theory concepts to perform a highly efficient transfer of RGB-D data from a client (robot) to a server (cloud). We show that this approach m...

This paper presents a new nearest neighbor (NN) retrieval framework: robust sparse hashing (RSH). Our approach is inspired by the success of dictionary learning for sparse coding. Our key idea is to sparse code the data using a learned dictionary, and then to generate hash codes out of these sparse codes for accurate and fast NN retrieval. But, dir...

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observa...

Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. O...

In this paper, we introduce a new covariance based feature descriptor to be used on "colored" point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, there is not yet a clear consensus on how to best tackle "colored" point clouds. We present the notion of a covariance on RGB-...

Object segmentation and classification is an important and difficult task in robotic vision. The task is complicated even further when the different objects are partially or completely occluded. Allowing a robot to take measurements from varying points of view can help in alleviating or completely removing occlusions. A robot equipped with an RGB-D...