Paths of Least Flow-Resistance: Characterization for the Optimization of Synthetic Tissue Scaffold Design. - Timothy Kline; Erik L. Ritman (ISBI, 2009)

Algorithm-Enabled Low-Dose Micro-CT Imaging. - Xiao Han; Junguo Bian; Diane R. Eaker; Timothy Kline; Emil Y. Sidky; Erik L. Ritman; Xiaochuan Pan (IEEE Trans. Medical Imaging, 2011)

Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning. - Zeynettin Akkus; Issa Ali; Jirí Sedlár; Timothy L. Kline; Jay P. Agrawal; Ian F. Parney; Caterina Giannini; Bradley J. Erickson (CoRR, 2016)

Toolkits and Libraries for Deep Learning. - Bradley J. Erickson; Panagiotis Korfiatis; Zeynettin Akkus; Timothy Kline; Kenneth Philbrick (J. Digit. Imaging, 2017)

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys. - Timothy L. Kline; Panagiotis Korfiatis; Marie E. Edwards; Jaime D. Blais; Frank S. Czerwiec; Peter C. Harris; Bernard F. King; Vicente E. Torres; Bradley J. Erickson (J. Digit. Imaging, 2017)

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. - Panagiotis Korfiatis; Timothy L. Kline; Daniel H. Lachance; Ian F. Parney; Jan C. Buckner; Bradley J. Erickson (J. Digit. Imaging, 2017)

Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients. - Panagiotis Korfiatis; Timothy L. Kline; Bradley J. Erickson (Computer-Aided Diagnosis, 2018)

Segmenting New Image Acquisitions Without Labels. - Timothy L. Kline (ISBI, 2019)

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. - Kenneth A. Philbrick; Alexander D. Weston; Zeynettin Akkus; Timothy L. Kline; Panagiotis Korfiatis; Tomas Sakinis; Petro M. Kostandy; Arunnit Boonrod; Atefeh Zeinoddini; Naoki Takahashi; Bradley J. Erickson (J. Digit. Imaging, 2019)

Improving Domain Generalization in Segmentation Models with Neural Style Transfer. - Timothy L. Kline (ISBI, 2021)

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning. - Adriana V. Gregory; Deema A. Anaam; Andrew J. Vercnocke; Marie E. Edwards; Vicente E. Torres; Peter C. Harris; Bradley J. Erickson; Timothy L. Kline (J. Digit. Imaging, 2021)

Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification. - Timothy L. Kline; Felipe Kitamura; Ian Pan; Amine M. Korchi; Neil A. Tenenholtz; Linda Moy; Judy Wawira Gichoya; Igor Santos; Steven Blumer; Misha Ysabel Hwang; Kim-Ann Git; Abishek Shroff; et al. (CoRR, 2022)

Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning. - Darryl E. Wright; Cole J. Cook; Jason Klug; Panagiotis Korfiatis; Timothy L. Kline (CoRR, 2022)

Shape Aware Automatic Region-of-Interest Subdivisions. - Timothy L. Kline (CoRR, 2022)

Quantifying and Visualizing Vascular Branching Geometry with Micro-CT: Normalization of Intra- and Inter-Specimen Variations. - Timothy L. Kline (CoRR, 2022)

A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging. - Weiguo Cao; Benjamin Howe; Nicholas Rhodes; Sumana Ramanathan; Panagiotis Korfiatis; Kimberly Amrami; Robert Spinner; Timothy Kline (MICCAI (8), 2023)

AI in the Loop - Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines. - Harrison C. Gottlich; Panagiotis Korfiatis; Adriana V. Gregory; Timothy L. Kline (CoRR, 2023)

Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization. - Timothy L. Kline; Sumana Ramanathan; Harrison C. Gottlich; Panagiotis Korfiatis; Adriana V. Gregory (CoRR, 2023)

Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging. - Darryl E. Wright; Adriana V. Gregory; Deema A. Anaam; Sepideh Yadollahi; Sumana Ramanathan; Kafayat A. Oyemade; Reem Alsibai; Heather Holmes; Harrison C. Gottlich; Cherie-Akilah G. Browne; Sarah L. Cohen Rassier; Isabel Green; et al. (CoRR, 2023)

Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. - Mana Moassefi; Pouria Rouzrokh; Gian Marco Conte; Sanaz Vahdati; Tianyuan Fu; Aylin Tahmasebi; Mira Younis; Keyvan Farahani; Amilcare Gentili; Timothy Kline; Felipe C. Kitamura; Yuankai Huo; et al. (J. Imaging Inform. Medicine, 2023)

Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study. - Harrison C. Gottlich; Adriana V. Gregory; Vidit Sharma; Abhinav Khanna; Amr U. Moustafa; Christine M. Lohse; Theodora A. Potretzke; Panagiotis Korfiatis; Aaron M. Potretzke; Aleksandar Denic; Andrew D. Rule; Naoki Takahashi; et al. (J. Digit. Imaging, 2023)

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models. - Farhad Maleki; Linda Moy; Reza Forghani; Tapotosh Ghosh; Katie L. Ovens; Steve Langer; Pouria Rouzrokh; Bardia Khosravi; Ali Ganjizadeh; Daniel I. Warren; Roxana Daneshjou; Mana Moassefi; et al. (CoRR, 2024)

A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images. - Peilong Wang; Timothy L. Kline; Andy D. Missert; Cole J. Cook; Matthew R. Callstrom; Alex Chan; Robert P. Hartman; Zachary S. Kelm; Panagiotis Korfiatis (CoRR, 2024)