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Toward high-throughput artificial intelligence-based segmentation in oncological PET imaging

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arXiv 2021年

作者： Yousefirizi, Fereshteh Jha, Abhinav K. Brosch-Lenz, Julia Saboury, Babak Rahmim, Arman Department of Integrative Oncology BC Cancer Research Institute VancouverBC Canada Department of Biomedical Engineering Washington University St. LouisMO United States Mallinckrodt Institute of Radiology Washington University in St. Louis United States Department of Radiology and Imaging Sciences Clinical Center National Institutes of Health BethesdaMD United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Department of Radiology Hospital of the University of Pennsylvania PhiladelphiaPA United States Departments of Radiology and Physics University of British Columbia Canada Provincial Medical Imaging Physicist BC Cancer BC Cancer Research Institute

Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks (CNNs) have shown impressive results and potential towards fully automated segmentation in medical imaging, and particularly PET imaging. To cope with the limited access to annotated data needed in supervised AI methods, given tedious and prone-to-error manual delineations, semi-supervised and unsupervised AI techniques have also been explored for segmentation of tumors or normal organs in single and bi-modality scans. This work provides a review of existing AI techniques for segmentation tasks and the evaluation criteria for translational AI-based segmentation efforts towards routine adoption in clinical workflows. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear medicine

Three-dimensional bioprinting of gelatin methacryloyl (GelMA)

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Bio-Design and Manufacturing 2018年第4期1卷 215-224页

作者： Guoliang Ying Nan Jiang Cunjiang Yu Yu Shrike Zhang Division of Engineering in Medicine Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolCambridgeMA 02139USA School of Materials Science and Engineering Wuhan Institute of TechnologyWuhan 430205People's Republic of China School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02139USA Departments of Mechanical Engineering Electrical and Computer EngineeringBiomedical EngineeringMaterials Science and Engineering ProgramThe Texas Center for SuperconductivityUniversity of HoustonHoustonTX 77204USA

The three-dimensional (3D)bioprinting technology has progressed tremendously over the past *** controlling the size, shape,and architecture of the bioprinted constructs,3D bioprinting allows for the fabrication of tissue/organ-like constructs with strong structural-functional similarity with their in vivo counterparts at high *** bioink,a blend of biomaterials and living cells possessing both high biocompatibility and printability,is a critical component of *** particular, gelatin methacryloyl (GelMA)has shown its potential as a viable bioink material due to its suitable biocompatibility and readily tunable physicochemical *** GelMA-based bioinks and relevant bioprinting strategies for GelMA bioprinting are briefly reviewed.

关键词： Bioprinting Bioink Gelatin methacryloyl (GelMA) Biofabrication -Tissue engineering Tissue model

Unravelling the origins of anomalous diffusion: from molecules to migrating storks

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arXiv 2021年

作者： Vilk, Ohad Aghion, Erez Avgar, Tal Beta, Carsten Nagel, Oliver Sabri, Adal Sarfati, Raphael Schwartz, Daniel K. Weiss, Matthias Krapf, Diego Nathan, Ran Metzler, Ralf Assaf, Michael Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem91904 Israel Movement Ecology Lab Department of Ecology Evolution and Behavior Alexander Silberman Institute of Life Sciences Faculty of Science The Hebrew University of Jerusalem Jerusalem91904 Israel Departments of Physics and Chemistry University of Massachusetts BostonMA02125 United States Wildlife Space-Use Ecology Lab Department of Wildland Resources and Ecology Center Utah State University LoganUT84332 United States Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Experimental Physics I University of Bayreuth BayreuthD-95440 Germany Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Department of Electrical and Computer Engineering School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States

Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating birds. Using data from multiple empirical systems, spanning 12 orders of magnitude in length and 8 orders of magnitude in time, we employ a method to detect the individual underlying origins of anomalous diffusion and transport in the data. This method decomposes anomalous transport into three primary effects: long-range correlations ("Joseph effect"), fat-tailed probability density of increments ("Noah effect"), and nonstationarity ("Moses effect"). We show that such a decomposition of real-life data allows to infer nontrivial behavioral predictions, and to resolve open questions in the fields of single particle tracking in living cells and movement ecology. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecules

Priming with specific context improves large language model performance on nuclear cardiology board preparation test

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Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 2025年 102269页

作者： Waseem Hijazi Valerie Builoff Aditya Killekar Aakash Shanbhag Robert Jh Miller Damini Dey Joanna X Liang Kathleen Flood Daniel Berman Jamieson M Bourque Lawrence M Phillips Panithaya Chareonthaitawee Piotr J Slomka Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA. Department of Cardiac Sciences University of Calgary Calgary AB Canada. American Society of Nuclear Cardiology Fairfax Virginia USA. Division of Cardiovascular Medicine and Radiology University of Virginia Health System Charlottesville VA USA. Leon H. Charney Division of Cardiology Department of Medicine NYU Grossman School of Medicine New York NY USA. Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA. Electronic address: Piotr.Slomka@***.

Reprogrammable 3D Mesostructures Through Compressive Buckling of Thin Films with Prestrained Shape Memory Polymer

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Acta Mechanica Solida Sinica 2018年第5期31卷 589-598页

作者： Xiaogang Guo Zheng Xu Fan Zhang Xueju Wang Yanyang Zi John A. Rogers Yonggang Huang Yihui Zhang Department of Engineering Mechanics Center for Mechanics and Materials and Center for Flexible Electronics Technology AML Tsinghua University Beijing 100084 China State Key Laboratory for Manufacturing and Systems Engineering School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 China Simpson Querrey Institute and Feinberg Medical School Center for Bio-Integrated Electronics Northwestern University Evanston IL 60208 USA Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Center for Bio-Integrated Electronics Simpson Querrey Institute for Nano/biotechnology Northwestern University Evanston IL 60208 USA Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Center for Bio-Integrated Electronics Northwestern University Evanston IL 60208 USA

The mechanically guided assembly that relies on the compressive buckling of strate- gically patterned 2D thin films represents a robust route to complex 3D mesostructures in advanced materials and even functional micro-devices. Based on this approach, formation of complex 3D configurations with suspended curvy features or hierarchical geometries remains a challenge. In this paper, we incorporate the prestrained shape memory polymer in the 2D precur- sor design to enable local rolling deformations after the mechanical assembly through compressive buckling. A theoretical model captures quantitatively the effect of key design parameters on local rolling deformations. The combination of precisely controlled global buckling and local rolling expands substantially the range of accessible 3D configurations. The combined experimental and theoretical studies over a dozen of examples demonstrate the utility of the proposed strategy in achieving complex reprogrammable 3D mesostructures.

关键词： Mechanically guided 3D assembly Reprogrammable 3D mesostructures Shapememory polymer Buckling Rolling

Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks

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arXiv 2019年

作者： Wen, Bihan Ravishankar, Saiprasad Pfister, Luke Bresler, Yoram School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Departments of Computational Mathematics Science and Engineering and Biomedical Engineering Michigan State University East LansingMI48824 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-ChampaignIL61801 United States

Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in known analytical dictionaries or transform domains, methods using learned image models for reconstruction have become popular. The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i.e., blind CS (BCS). Besides the well-known synthesis dictionary model, recent advances in transform learning (TL) provide an efficient alternative framework for sparse modeling in MRI. TL-based methods enjoy numerous advantages including exact sparse coding, transform update, and clustering solutions, cheap computation, and convergence guarantees, and provide high-quality results in MRI compared to popular competing methods. This paper provides a review of some recent works in MRI reconstruction from limited data, with focus on the recent TL-based methods. A unified framework for incorporating various TL-based models is presented. We discuss the connections between transform learning and convolutional or filterbank models and corresponding multilayer extensions, with connections to deep learning. Finally, we discuss recent trends in MRI, open problems, and future directions for the field. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

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arXiv 2019年

作者： Ravishankar, Saiprasad Chul Ye, Jong Fessler, Jeffrey A. Departments of Computational Mathematics Science and Engineering and Biomedical Engineering Michigan State University East LansingMI48824 United States Daejeon Korea Republic of Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

An Adaptive Boosting Strategy for GLCM-CNN Model in Differentiating the Malignant from Benign Polyps

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An Adaptive Boosting Strategy for GLCM-CNN Model in Differen...

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Shu Zhang Zhengrong Liang Weiguo Cao Yongfeng Gao Perry J. Pickhardt Department of Radiology Stony Brook University Stony Brook NY USA Departments of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony Brook University Stony Brook NY USA Department of Radiology University of Wisconsin Madison WI USA

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Recently, deep learning such as Convolutional Neural Network (CNN) has shown its superior for the task of image classification. However, it faces great challenges in the medical imaging field, especially for tumor classification in computer-aided diagnosis, because there are many uncertainties of lesions including their size, scaling factor, rotation, shapes, etc. Hence, texture pattern is an option to be fed into the CNN model and among the texture patterns, gray level co-occurrence matrix (GLCM) can be chosen as the texture pattern for its several good properties such as uniform size, shape invariance, posture robustness, scaling invariance. For a 3D volume sample, instead of studying on the raw images, 13 independent GLCMs could be extracted based on the 13 digital distinct directions, and different sampling displacements have been recorded accordingly. Therefore, considering this multi-scale sampling is hypothesized to be optimal compared to without considering the multi-scale nature, we proposed an adaptive boosting learning (ABL) to examine the proposed multi-scale hypothesis. Following the multi-scale nature, the 13 directions can be grouped as three by different sampling displacements, and the ABL is applied to learn the best classification performance from the 3 subgroups. By comparing our ABL with and without the multi-scale nature, our experiments obtained a gain of 0.5~3.7% in terms of AUC (area under the ROC curve) for polyp classification, eventually reaching AUC of 91.13% for a small database of 63 samples with pathological reports. The gains reveal that the multi-scale GLCM-CNN model with ABL strategy can reduce the challenges for classification of limited number of tumors in medical imaging field.

关键词： biomedical imaging Cancer Adaptation models Boosting Computed tomography Shape

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET images

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arXiv 2020年

作者： Leung, Kevin H. Marashdeh, Wael Wray, Rick Ashrafinia, Saeed Pomper, Martin G. Lab, Jha Rahmim, Arman Jha, Abhinav K. Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States The Russell H. Morgan Department of Radiology Johns Hopkins University BaltimoreMD United States Department of Radiology and Nuclear Medicine Jordan University of Science and Technology Arramtha Jordan Memorial Sloan Kettering Cancer Center Greater New York City AreaNY United States Department of Electrical & Computer Engineering Johns Hopkins University BaltimoreMD United States Departments of Radiology and Physics University of British Columbia VancouverBC Canada Department of Biomedical Engineering Mallinckrodt Institute of Radiology Washington University in St. Louis St. LouisMO United States

An important need exists for reliable PET tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. The purpose of this study was to develop an automated physics-guided deep-learning-based PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical training data with ground-truth tumor boundaries in PET imaging. We propose a three-module PET-segmentation framework in the context of segmenting primary tumors in 3D 18F-fluorodeoxyglucose (FDG)-PET images of patients with lung cancer on a per-slice basis. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework's accuracy, generalizability to different scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing the number of training images were quantitatively evaluated using Dice similarity coefficient (DSC) and several other metrics. The framework yielded reliable performance in both simulated (DSC: 0.87 (95% CI: 0.86, 0.88)) and patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely used semi-automated approaches, accurately segmented relatively small tumors (smallest segmented cross-section was 1.83 cm2), generalized across five PET scanners (DSC: 0.74 (95% CI: 0.71, 0.76)), was relatively unaffected by PVEs, and required low training data (training with data from even 30 patients yielded DSC of 0.70 (95% CI: 0.68, 0.71)). A modular deep-learning-based fram

关键词： Scanning