Nicholas Heller

• Doctor of Philosophy
• Research Associate at Cleveland Clinic

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...

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...

INTRODUCTION Since its introduction, the RENAL nephrometry score has proven to be an effective tool in medical decision-making and surgical planning for renal tumors and has been shown to be predictive of several important oncologic and operative outcomes. However, some studies have reported high interobserver variability, particularly within the e...

Background and Objectives In most patients, the renal parenchymal volumes in each kidney directly correlate with function and can be used as a proxy to determine split renal function (SRF). This simple principle forms the basis for parenchymal volume analysis (PVA) with semiautomated software, which can be leveraged to predict SRF and new‐baseline...

Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). H...

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...

This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis: generating high-quality reference annotations. Previous research has focused on crowdsourcing as a means of outsourcing annotations. However, little attention has so far...

This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis: generating high-quality reference annotations. Previous research has focused on crowdsourcing as a means of outsourcing annotations. However, little attention has so far...

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. Th...

In recent years, many new targeted and immuno-therapies have entered the cancer treatment landscape with the potential to drastically improve patient outcomes; however, selecting the optimal drug target especially as the disease progresses becomes increasingly important. Liquid biopsies show great promise in providing insights on the current diseas...

Although mammograms have been a useful tool since the first half of the 20th century and have an acceptable sensitivity (87%) in the general population, that drops to <50% in women with dense breasts making it an ineffective screening tool in this population. Cell free DNA (cfDNA) has proven to be an inadequate alternative in this population, and w...

Recent research increasingly validates the early dissemination of tumor cells in peripheral blood during the nascent stages of cancer, often preceding clinical identification of the primary tumor. This emerging evidence emphasizes the potential of Circulating Tumor Cells (CTCs) as early indicators for cancer. Despite their significance, CTCs are ex...

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...

Background: Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations. Objective: Our aim is to evaluate the performance of a CNN model as a proof of concept in discriminat...

We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the...

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...

In recent years, liquid biopsies have emerged to provide improved insights on the current disease state of a patient, but they generally rely on a small number of protein markers from immunofluorescent staining or cell-free DNA, which provides limited information especially around drug targets. As more targeted therapies and immunotherapies enter t...

Organoids are a widely used 3D culture system that has the potential to recapitulate the profile and phenotype of physiological cells. The culture of circulating tumor cells (CTCs) in this 3D system not only mimics the tumor physiological environment but allows researchers to test therapeutic responses to certain drugs based on a patient's own canc...

In recent years, liquid biopsies have emerged to provide improved insights on the current disease state of a patient, but they generally rely on a small number of protein markers from immunofluorescent staining or cell-free DNA, which provides limited information especially around drug targets. As more targeted and immune therapies enter the market...

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a...

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...

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in...

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...

Trial design This was a randomized controlled trial. Background Intraoperative errors correlate with surgeon skill and skill declines with intervals of inactivity. The goals of this research were to identify the optimal virtual reality (VR) warm-up curriculum to prime a surgeon's technical skill and validate benefit in the operating room. Materia...

Modern AI systems have achieved impressive performance and are poised to have a substantial impact on urology. It's important for clinicians to get actively involved in the development and validation of these systems to ensure that their impact is positive.

This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtu...

The original version of this book was revised. The following corrections were implemented: The acronym was corrected to “MIL3ID” throughout the book. The equation on page 5 of Chapter 14 was modified to improve its accuracy and readability.

PurposeSummary score metrics, either from crowds of non-experts, faculty surgeons or from automated performance metrics, have been trusted as the prevailing method of reporting surgeon technical skill. The aim of this paper is to learn whether there exist significant fluctuations in the technical skill assessments of a surgeon throughout long durat...

Purpose: Ischemic priapism is a urological emergency that requires prompt intervention to preserve erectile function. Characteristics that influence escalation to surgical intervention remain unclear. Our objective was to identify factors and develop machine learning models to predict which men presenting with ischemic priapism will require shunti...

Purpose: Previous research has shown that obtaining non-expert crowd evaluations of surgical performances concords with the gold standard of expert surgeon review, and that faster playback speed increases ratings for videos of higher-skilled surgeons in laparoscopic simulation. The aim of this research is to extend this investigation to real surger...

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...

This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Cor...

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...

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...

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...

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...

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...

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...