Efficient and accurate assignment of journal submissions to suitable associate editors (AEs) is critical in maintaining review quality and timeliness, particularly in high-volume, rapidly evolving fields such as medical imaging. This study investigates the feasibility of leveraging large language models for AE-paper matching in IEEE Transactions on Medical Imaging. An AE database was curated from historical AE assignments and AE-authored publications, and extracted six key textual components from each paper title, four categories of structured keywords, and abstracts. ModernBERT was employed locally to generate high-dimensional semantic embeddings, which were then reduced using principal component analysis (PCA) for efficient similarity computation. Keyword similarity, derived from structured domain-specific metadata, and textual similarity from ModernBERT embeddings were combined to rank the candidate AEs. Experiments on internal (historical assignments) and external (AE Publications) test sets showed that keyword similarity is the dominant contributor to matching performance. Contrarily, textual similarity offers complementary gains, particularly when PCA is applied. Ablation studies confirmed that structured keywords alone provide strong matching accuracy, with titles offering additional benefits and abstracts offering minimal improvements. The proposed approach offers a practical, interpretable, and scalable tool for editorial workflows, reduces manual workload, and supports high-quality peer reviews.
This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
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Mathematical modelling of blood flow through an artery with multiple stenoses and poststenotic dilatations is surveyed in this paper. A set of equations describes the resistance to flow ratio of an artery. Analytic solutions are based on homogenous and irrotational flow through mathematically constructed vessels. Variations in resistance to flow ratio are subjected to alterations in flow behaviour index, structural variations in relation to magnitude of vessel stenosis and multiple abnormal segments. Our analytical framework examines the effects that variability in arterial wall geometry have on the blood flow resistance. The results may aid the angiographic assessment of occlusion due to lesion development in atherosclerotic coronary arteries. References K. C. Ang, J. Mazumdar, and I. Hamilton Craig. A computational model for blood flow through highly curved arteries with asymmetric stenoses. Aust. Phys. Eng. Sci. Med., 20(3):152--163, 1997. J. Forrester and D. Young. Flow through a converging diverging tube and its implications in occulsive vascular disease--i. J. Biomech., 3:297--305, 1970. J. Forrester and D. Young. Flow through a converging diverging tube and its implications in occulsive vascular disease--ii. J. Biomech., 3:307--316, 1970. H. W. Hoogstraten, J. G. Kootstra, B. Hillen, J. K. Krijger, and P. J. Wensing. Numerical simulation of blood flow in an artery with two successive bends. J Biomech., 29(8):1075--1083, 1996. I. Kompatsiaris, D. Tzovaras, V. Koutkias, and M. G. Strintzis. Deformable boundary detection of stents in angiographic images. IEEE Transactions on Medical Imaging, 19(6):652--662, 2000. G. Koning, J. C. Tuinenburg, E. Hekking, J. Peelen, A. W. M. van Weert, D. Bergkamp, B. Goedhart, and J. H. C. Reiber. A novel measurement technique to assess the effects of coronary brachytheraphy in clinical trials. IEEE Transactions on Medical Imaging, 21(10):1254--1263, 2002. M. Kretowski, Y. Rolland, J. Bezy-Wendlin, and J-L. Coatrieux. Physiologically based modelling of 3-D vascular networks and CT scan angiography. IEEE Transactions on Medical Imaging, 22(2):248--257, 2003. D. MacDonald. On steady flow through modelled vascular stenoses. J. Biomechanics, 12:13--20, 1979. J. N. Mazumdar. Biofluid Mechanics. World Scientific, N. J. USA, 1992. J. A. Moore, D. A. Steinman, S. Prakash, K. W. Johnston, and C. R. Ethier. A numerical study of blood flow patterns in anatomically realistic and simplified end-to-side anastomoses. J. Biomech Eng, 121:265--272, 1999. K. Perktolda, M. Hofera, G. Rappitscha, M. Loewa, B. D. Kubana, and M. H. Friedmana. Validated computation of physiologic flow in a realistic coronary artery branch. J Biomech, 31(3):217--228, 1997. B. Pincombe and J. N. Mazumdar. A mathematical study of blood flow through viscoelastic walled stenosed arteries. Aust. Phys. Eng. Sci. Med., 18:81--88, 1995. B. Pincombe and J. N. Mazumdar. The effects of post-stenotic dilatations on the flow of a blood analogue through stenosed coronary arteries. Mathl. Comput. Modelling, 25:57--70, 1997. B. Pincombe and J. N. Mazumdar. Herschel--Bulkley and casson flow through viscoelastic walled stenosed arteries. EMAC'98, The Institute of Engineers (Australia), E. O. Tuck and J. A. K. Stott (eds.), pages 399--402, 1998. B. Pincombe and J. N. Mazumdar. Numerical model of power law flow through an atherosclerotic artery, pages 563--570. CTAC'97, World Scientific Press, B. J. Noye, M. D. Teubner, 1998. B. Pincombe and J. N. Mazumdar. Techniques for the study of blood flow through both constrictions and post-stenotic dilatations in arteries, Handbook of Computational Methods in Biomaterials, Biotechnology and Biomedical Systems. Kluwer Academic, 2002. B. Pincombe, J. N. Mazumdar, and I. Hamilton-Craig. Effects of multiple stenoses and post-stenotic dilatation on non-newtonian blood flow in small arteries. Med. Biol. Eng. Comput., 37(5):595--599, 1999. J. Poiseuille. Observations of blood flow. Ann. Sci. Naturelles Serie, 5:2, 1836. M. C. Potter, D. C. Wiggert, and M. Hondzo. Mechanics of Fluids. Number 261-263. Prentice-Hall International, Inc, USA, 2nd edition, 1997. J. Soulis, T. Farmakis, G. Giannoglou, and G. Louridas. Wall shear stress in normal left coronary artery tree. J. Biomech, 39(4):742--749, 2006. S. C. Tjin, S. L. Ng, and K. T. Soo. In vivo Doppler shift measurements using multimode fibre optic catheters. IEEE Transaction Biomedical Engineering, 45:1272--1278, 1998. K. K. L. Wong, J. Mazumdar, B. Pincombe, S. G. Worthley, P. Sanders, and D. Abbott. Theoretical modeling of micro-scale biological phenomena in human coronary arteries. Medical and Biological Engineering and Computing, 44(11):971--982, 2006. doi:10.1007/s11517-006-0113-6 S. G. Worthley, G. Helft, V. Fuster, Z. A. Fayad, J. T. Fallon, J. I. Osende, M. Roque, M. Shinnar, A. G. Zaman, O. J. Rodriguez, et al. High resolution ex vivo magnetic resonance imaging of in situ coronary and aortic atherosclerotic plaque in a porcine model. Atherosclerosis, 150(2):321--329, 2000. S. G. Worthley, G. Helft, V. Fuster, A. G. Zaman, Z. A. Fayad, J. T. Fallon, and J. J. Badimon. Serial in vivo MRI documents arterial remodeling in experimental atherosclerosis. Circulation, 101(6):586--589, 2000. S. G. Worthley, H. M. Omar-Farouque, G. Helft, and I. T. Meredith. Coronary artery imaging in the new millennium. Heart Lung Circ., 11(1):19--25, 2002. P. J. Yim, J. J. Cebral, R. Mullick, H. B. Marcos, and P. L. Choyke. Vessel surface reconstruction with a tubular deformable model. IEEE Transactions on Medical Imaging, 20(1):1411--1421, 2001.
The notion of fuzzy connectedness captures the idea of "hanging-togetherness" of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1,000s of images in a variety of applications. In the previous framework, a fuzzy connected object is defined with a threshold on the strength of connectedness. We introduce the notion of relative connectedness that overcomes the need for a threshold and that leads to more effective segmentations. The central idea is that an object gets defined in an image because of the presence of other co-objects. Each object is initialized by a seed element. An image element c is considered to belong to that object with respect to whose reference image element c has the highest strength of connectedness. In this fashion, objects compete among each other utilizing fuzzy connectedness to grab membership of image elements. We present a theoretical and algorithmic framework for defining objects via relative connectedness and demonstrate utilizing the theory that the objects defined are independent of reference elements chosen as long as they are not in the fuzzy boundary between objects. An iterative strategy is also introduced wherein the strongest relative connected core parts are first defined and iteratively relaxed to conservatively capture the more fuzzy parts subsequently. Examples from medical imaging are presented to illustrate visually the effectiveness of relative fuzzy connectedness. A quantitative mathematical phantom study involving 160 images is conducted to demonstrate objectively the effectiveness of relative fuzzy connectedness. <br> <h3 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Disclaimer</h3> <blockquote xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A claim of priority in research and publication appeared on page 1486 in the paper "Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation" by J.K. Udupa, P.K. Saha, and' R.A. Lotufo (IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1485-1500,Nov. 2002) with respect to the paper "Multiseeded Segmentation Using Fuzzy Connectedness by G.T. Herman and B.M. Carvalho" (IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 5, pp. 460-474, May 2001). Furthermore, the wording of this claim suggests professional misconduct on the part of G.T. Herman and B.M. Carvalho. Responsibility for the content of published papers rests with the authors. The peer review process is intended to determine the overall significance of the technical contribution of a manuscript. The peer review process does not provide a way to validate every statement in a manuscript. In particular, the IEEE has not validated the claim referred to in the first paragraph above. The IEEE regrets publishing this unauthenticated statement and the pain that such publication may have caused to G.T. Herman and B.M. Carvalho. </blockquote>
Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). To explore the effects of non-movement-related signal changes on registration error, we performed several previously proposed test simulations (Freire et al., IEEE Transactions on Medical Imaging 2002;21:470-484) to evaluate the performance of MCICA and compare it with the conventional square-of-difference-based measures such as LS-SPM and LS-AIR. We demonstrate that for both simulated data and real fMRI images, the proposed MCICA method performs favorably. Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.
Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.
From the Publisher: Yao Wang received the B.S. and M.S. degrees in electrical engineering from Tsinghua University, Beijing, China, in 1983 and 1985, respectively, and the Ph.D. degree in electrical and computer engineering from the University of California at Santa Barbara in 1990. Since 1990, she has been with the Faculty of Electrical Engineering, Polytechnic University, Brooklyn, NY. Her research areas include video communications, multimedia signal processing, and medical imaging. She has authored and co-authored over 100 papers in journals and conference proceedings. She is a senior member of IEEE and has served as an Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology and the IEEE Transactions on Multimedia. She won the Mayor's Award of the City of New York for Excellence in Science and Technology in the Young Investigator category in 2000. Jvrn Ostermann studied electrical engineering and communications engineering at the University of Hannover and Imperial College London, respectively. He received Dipl.-Ing. and Dr.-Ing. from the University of Hannover in 1988 and 1994, respectively. He has been a staff member with Image Processing and Technology Research, AT&T Labs>Research since 1996, where he is engaged in research on video coding, shape coding, multi-modal human-computer interfaces with talking avatars, standardization, and image analysis. He is a German National Foundation scholar. In 1998, he received the AT&T Standards Recognition Award and the ISO award. He is a member of the IEEE, the IEEE Technical Committee on Multimedia Signal Processing, and chair of the IEEE CAS Visual Signal Processing and Communications (VSPC) TechnicalCommittee. Ya-Qin Zhang received the B.S. and M.S. degrees in electrical engineering from the University of Science and Technology of China (USTC) in 1983 and 1985, respectively, and the Ph.D. degree from George Washington University in 1989. He is currently the Managing Director of Microsoft Research in Beijing, after leaving his post as the Director of Multimedia Technology Laboratory at the Sarnoff Corporation in Princeton, NJ (formerly the David Sarnoff Research Center, and RCA Laboratories). He has been engaged in research and commercialization of MPEG2/DTV, MPEG4/VLBR, and multimedia information technologies. He has authored and co-authored over 200-refereed papers in leading international conference proceedings and journals. He has been granted over 40 U.S. patents in digital video, Internet, multimedia, wireless and satellite communications. He was the Editor-in-Chief of the IEEE Transactions on Circuits and Systems for Video Technology from 1997 to 1999. He is a Fellow of the IEEE.
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
There is controversy over the nature of the disturbance in brain development that underpins attention-deficit/hyperactivity disorder (ADHD). In particular, it is unclear whether the disorder results from a delay in brain maturation or whether it represents a complete deviation from the template of typical development. Using computational neuroanatomic techniques, we estimated cortical thickness at >40,000 cerebral points from 824 magnetic resonance scans acquired prospectively on 223 children with ADHD and 223 typically developing controls. With this sample size, we could define the growth trajectory of each cortical point, delineating a phase of childhood increase followed by adolescent decrease in cortical thickness (a quadratic growth model). From these trajectories, the age of attaining peak cortical thickness was derived and used as an index of cortical maturation. We found maturation to progress in a similar manner regionally in both children with and without ADHD, with primary sensory areas attaining peak cortical thickness before polymodal, high-order association areas. However, there was a marked delay in ADHD in attaining peak thickness throughout most of the cerebrum: the median age by which 50% of the cortical points attained peak thickness for this group was 10.5 years (SE 0.01), which was significantly later than the median age of 7.5 years (SE 0.02) for typically developing controls (log rank test chi(1)(2) = 5,609, P < 1.0 x 10(-20)). The delay was most prominent in prefrontal regions important for control of cognitive processes including attention and motor planning. Neuroanatomic documentation of a delay in regional cortical maturation in ADHD has not been previously reported.
As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the theme of deep tomographic reconstruction, the second special issue is assembled to reflect the status and momentum of this rapidly emerging field. In this editorial, we provide a brief background illustrating the motivation for the development of network-based, data-driven, and learning-oriented reconstruction methods, summarize the included papers, and report our verification of the shared deep learning codes. Finally, we discuss several important research topics to facilitate further investigation and collaboration.
The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.