Erica M. Rutter

Works (9)

Updated: April 23rd, 2025 20:39

2021 article

Multitask neural networks for predicting bladder pressure with time series data

Warrier, S., Rutter, E. M., & Flores, K. B. (2021, November 9). Biomedical Signal Processing and Control.

By: S. Warrier n, E. Rutter* & K. Flores n

author keywords: Multitask neural networks; Recurrent neural networks; Time series data; Bladder dysfunction; Bladder contraction; Spectrogram
topics (OpenAlex): Urinary Bladder and Prostate Research; Heart Rate Variability and Autonomic Control; Urinary Tract Infections Management
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Source: Web Of Science
Added: November 29, 2021

2020 article

A tutorial review of mathematical techniques for quantifying tumor heterogeneity

Everett, R., Flores, K. B., Henscheid, N., Lagergren, J., Larripa, K., Li, D., … Rutter, E. M. (2020, January 1). Mathematical Biosciences & Engineering.

By: R. Everett*, K. Flores n, N. Henscheid *, J. Lagergren n, K. Larripa*, D. Li *, J. Nardini n, P. Nguyen n, E. Pitman *, E. Rutter n

author keywords: cancer heterogeneity; mathematical oncology; tumor growth; glioblastoma multiforme; virtual populations; nonlinear mixed effects; spatiotemporal data; Bayesian estimation; generative; adversarial networks; non-parametric estimation; variational autoencoders; machine learning
MeSH headings : Bayes Theorem; Humans; Machine Learning; Models, Theoretical; Neoplasms; Precision Medicine
topics (OpenAlex): Radiomics and Machine Learning in Medical Imaging; Mathematical Biology Tumor Growth; Artificial Intelligence in Healthcare and Education
TL;DR: Several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data are reviewed, including virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. (via Semantic Scholar)
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Source: Web Of Science
Added: August 3, 2020

2020 article

Learning partial differential equations for biological transport models from noisy spatio-temporal data

Lagergren, J. H., Nardini, J. T., Lavigne, G. M., Rutter, E. M., & Flores, K. B. (2020, February 1). Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences.

By: J. Lagergren n, J. Nardini n, G. Lavigne n, E. Rutter n & K. Flores n

author keywords: numerical differentiation; equation learning; sparse regression; partial differential equations; parameter estimation; biological transport
topics (OpenAlex): Model Reduction and Neural Networks; Probabilistic and Robust Engineering Design; Gaussian Processes and Bayesian Inference; Neural Networks and Reservoir Computing; Tensor decomposition and applications
TL;DR: It is shown that the ANN methodology outperforms previous denoising methods, including finite differences and both local and global polynomial regression splines, in the ability to accurately approximate partial derivatives and learn the correct PDE model. (via Semantic Scholar)
Source: Web Of Science
Added: March 30, 2020

2018 article

Automated Object Tracing for Biomedical Image Segmentation Using a Deep Convolutional Neural Network

Rutter, E. M., Lagergren, J. H., & Flores, K. B. (2018, January 1). Lecture Notes in Computer Science.

By: E. Rutter n, J. Lagergren n & K. Flores n

topics (OpenAlex): Cell Image Analysis Techniques; AI in cancer detection; Image Processing Techniques and Applications
TL;DR: A novel methodology that uses CNNs for segmentation by mimicking the human task of tracing object boundaries is presented that is more accurate than CNNs alone and orders of magnitude faster than manual segmentation. (via Semantic Scholar)
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Source: Web Of Science
Added: August 12, 2019

2018 article

Estimating intratumoral heterogeneity from spatiotemporal data

Rutter, E. M., Banks, H. T., & Flores, K. B. (2018, May 8). Journal of Mathematical Biology.

By: E. Rutter n, H. Banks n & K. Flores n

author keywords: Glioblastoma multiforme; Random differential equation; Parameter estimation
MeSH headings : Antineoplastic Agents / therapeutic use; Brain Neoplasms / drug therapy; Brain Neoplasms / pathology; Cell Movement; Cell Proliferation; Computer Simulation; Glioblastoma / drug therapy; Glioblastoma / pathology; Humans; Mathematical Concepts; Models, Biological; Neoplasm Invasiveness / pathology; Phenotype; Probability; Spatio-Temporal Analysis; Tumor Burden / drug effects
topics (OpenAlex): Radiomics and Machine Learning in Medical Imaging; Ultrasound Imaging and Elastography; Medical Imaging Techniques and Applications
TL;DR: The predicted effect of treatment, specifically, chemotherapy, when assuming such a heterogeneous population and compare with predictions from a homogeneous cell population model are examined. (via Semantic Scholar)
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Source: Web Of Science
Added: December 17, 2018

2018 article

Simple multi-scale modeling of the transmission dynamics of the 1905 plague epidemic in Bombay

Pell, B., Phan, T., Rutter, E. M., Chowell, G., & Kuang, Y. (2018, April 16). Mathematical Biosciences.

By: B. Pell *, T. Phan *, E. Rutter n, G. Chowell * & Y. Kuang *

author keywords: Multi-scale modeling; Sub-exponential growth; Plague epidemic; 1905 Bombay plague; Mathematical modeling; Disease transmission dynamics
MeSH headings : Basic Reproduction Number; Confidence Intervals; Epidemics / history; Epidemics / statistics & numerical data; Forecasting / methods; History, 20th Century; Humans; India / epidemiology; Logistic Models; Mathematical Concepts; Models, Biological; Plague / epidemiology; Plague / history; Plague / transmission; Spatio-Temporal Analysis
topics (OpenAlex): Yersinia bacterium, plague, ectoparasites research; Zoonotic diseases and public health; Vibrio bacteria research studies
TL;DR: It is shown that it is possible for the growth of an epidemic in the early phase to be sub-exponential at sub-city level, while maintaining near exponential growth at an aggregated city level, and the rate of movement between districts can have a significant effect on the final epidemic size. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2018 article

Traveling Waves of a Go-or-Grow Model of Glioma Growth

Stepien, T. L., Rutter, E. M., & Kuang, Y. (2018, January 1). SIAM Journal on Applied Mathematics.

By: T. Stepien *, E. Rutter* & Y. Kuang *

author keywords: glioblastoma; tumor growth; go or grow; traveling wave; mathematical modeling
topics (OpenAlex): Mathematical Biology Tumor Growth; Microtubule and mitosis dynamics; Gene Regulatory Network Analysis
TL;DR: The first mathematical models of the spread of gliomas featured reaction-diffusionEquilibrium models, which showed clear trends in how tumor cells excessively proliferate and migrate. (via Semantic Scholar)
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Added: August 6, 2018

2017 article

Effects of non-physiological blood pressure artefacts on cerebral autoregulation

Mahdi, A., Rutter, E. M., & Payne, S. J. (2017, July 8). Medical Engineering & Physics.

By: A. Mahdi *, E. Rutter n & S. Payne *

author keywords: Cerebral autoregulation; Cerebral blood flow; Arterial blood pressure; Blood pressure artefacts
MeSH headings : Artifacts; Blood Flow Velocity / physiology; Blood Pressure / physiology; Blood Pressure Determination / methods; Cerebrovascular Circulation / physiology; Homeostasis / physiology; Humans; Reproducibility of Results; Sensitivity and Specificity
topics (OpenAlex): Traumatic Brain Injury and Neurovascular Disturbances; Optical Imaging and Spectroscopy Techniques; Radiation Dose and Imaging
TL;DR: It is shown that a sufficiently large saturation and square wave always result in ARI reaching the maximum value of 9, and the critical size of artefacts is characterized, defined as the minimum artefact size that, on average, leads to a 10% deviation of ARI. (via Semantic Scholar)
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Added: August 6, 2018

2017 article

Mathematical Analysis of Glioma Growth in a Murine Model

Rutter, E. M., Stepien, T. L., Anderies, B. J., Plasencia, J. D., Woolf, E. C., Scheck, A. C., … Kostelich, E. J. (2017, May 24). Scientific Reports.

By: E. Rutter n, T. Stepien *, B. Anderies *, J. Plasencia *, E. Woolf*, A. Scheck *, G. Turner *, Q. Liu * ...

MeSH headings : Animals; Cell Line, Tumor; Disease Models, Animal; Female; Glioma / genetics; Glioma / pathology; Humans; Mice; Models, Theoretical; Tumor Burden / genetics; Tumor Burden / physiology; Xenograft Model Antitumor Assays
topics (OpenAlex): Mathematical Biology Tumor Growth; MRI in cancer diagnosis; Glioma Diagnosis and Treatment
TL;DR: The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail, as well as errors in the mathematical model. (via Semantic Scholar)
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Added: August 6, 2018

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