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Multi-Scale Spatial-Temporal Attention Networks for Functional Connectome Classification IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-22 Youyong Kong, Xiaotong Zhang, Wenhan Wang, Yue Zhou, Yueying Li, Yonggui Yuan
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Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-22 Kunming Tang, Zhiguo Jiang, Kun Wu, Jun Shi, Fengying Xie, Wei Wang, Haibo Wu, Yushan Zheng
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Unsupervised Non-rigid Histological Image Registration Guided by Keypoint Correspondences Based on Learnable Deep Features with Iterative Training IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-21 Xingyue Wei, Lin Ge, Lijie Huang, Jianwen Luo, Yan Xu
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Optimized Excitation in Microwave-induced Thermoacoustic Imaging for Artifact Suppression IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-21 Qiang Liu, Weian Chao, Ruyi Wen, Yubin Gong, Lei Xi
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Moment-Consistent Contrastive CycleGAN for Cross-Domain Pancreatic Image Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-21 Zhongyu Chen, Yun Bian, Erwei Shen, Ligang Fan, Weifang Zhu, Fei Shi, Chengwei Shao, Xinjian Chen, Dehui Xiang
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Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-19 Yidan Feng, Sen Deng, Jun Lyu, Jing Cai, Mingqiang Wei, Jing Qin
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Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-19 M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. Van Sloun, Peter H. N. De With, Fons van der Sommen
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SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-space IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-19 Oscar Dabrowski, Jean-Luc Falcone, Antoine Klauser, Julien Songeon, Michel Kocher, Bastien Chopard, François Lazeyras, Sébastien Courvoisier
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Bi-Constraints Diffusion: A Conditional Diffusion Model with Degradation Guidance for Metal Artifact Reduction IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-15 Mengting Luo, Nan Zhou, Tao Wang, Linchao He, Wang Wang, Hu Chen, Peixi Liao, Yi Zhang
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AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-15 Cagan Alkan, Morteza Mardani, Congyu Liao, Zhitao Li, Shreyas S. Vasanawala, John M. Pauly
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S2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-15 Jialun Pei, Diandian Guo, Jingyang Zhang, Manxi Lin, Yueming Jin, Pheng-Ann Heng
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Domain-interactive Contrastive Learning and Prototype-guided Self-training for Cross-domain Polyp Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-14 Ziru Lu, Yizhe Zhang, Yi Zhou, Ye Wu, Tao Zhou
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Prompt-driven Latent Domain Generalization for Medical Image Classification IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-08-13 Siyuan Yan, Zhen Yu, Chi Liu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, Zongyuan Ge
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Mutual Information Guided Diffusion for Zero-Shot Cross-Modality Medical Image Translation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-29 Zihao Wang, Yingyu Yang, Yuzhou Chen, Tingting Yuan, Maxime Sermesant, Hervé Delingette, Ona Wu
Cross-modality data translation has attracted great interest in medical image computing. Deep generative models show performance improvement in addressing related challenges. Nevertheless, as a fundamental challenge in image translation, the problem of zero-shot learning cross-modality image translation with fidelity remains unanswered. To bridge this gap, we propose a novel unsupervised zero-shot
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Mixed Supervision of Histopathology Improves Prostate Cancer Classification From MRI IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-28 Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde, Jeffry P. Simko, Hao Nguyen, Thomas A. Hope, Peder E. Z. Larson, Kirti Magudia
Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy
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Toward Accurate Cardiac MRI Segmentation With Variational Autoencoder-Based Unsupervised Domain Adaptation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-28 Hengfei Cui, Yan Li, Yifan Wang, Di Xu, Lian-Ming Wu, Yong Xia
Accurate myocardial segmentation is crucial in the diagnosis and treatment of myocardial infarction (MI), especially in Late Gadolinium Enhancement (LGE) cardiac magnetic resonance (CMR) images, where the infarcted myocardium exhibits a greater brightness. However, segmentation annotations for LGE images are usually not available. Although knowledge gained from CMR images of other modalities with ample
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Multi-Path Fusion in SFCF-Net for Enhanced Multi-Frequency Electrical Impedance Tomography IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-27 Xiang Tian, Jian’an Ye, Tao Zhang, Liangliang Zhang, Xuechao Liu, Feng Fu, Xuetao Shi, Canhua Xu
Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To
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Instrument-Tissue Interaction Detection Framework for Surgical Video Understanding IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-26 Wenjun Lin, Yan Hu, Huazhu Fu, Mingming Yang, Chin-Boon Chng, Ryo Kawasaki, Cheekong Chui, Jiang Liu
Instrument-tissue interaction detection task, which helps understand surgical activities, is vital for constructing computer-assisted surgery systems but with many challenges. Firstly, most models represent instrument-tissue interaction in a coarse-grained way which only focuses on classification and lacks the ability to automatically detect instruments and tissues. Secondly, existing works do not
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Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis Using Histopathological Images IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-26 Mingxin Liu, Yunzan Liu, Pengbo Xu, Hui Cui, Jing Ke, Jiquan Ma
Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning
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Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-26 Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt, Marcel Bengs, Alexander Schlaefer, Miron Radu, Di Xu, Ke Sheng, Fabien Scalzo, Eric Marcus, Samuele Papa, Jonas Teuwen, Ernst Th. Scholten, Steven Schalekamp, Nils Hendrix, Colin Jacobs, Ward Hendrix, Clara I. Sánchez, Keelin Murphy
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of
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Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Kang Wang, Feiyang Zheng, Lan Cheng, Hong-Ning Dai, Qi Dou, Jing Qin
Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless
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Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Yu Guan, Chuanming Yu, Zhuoxu Cui, Huilin Zhou, Qiegen Liu
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UniChest: Conquer-and-Divide Pre-Training for Multi-Source Chest X-Ray Classification IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Tianjie Dai, Ruipeng Zhang, Feng Hong, Jiangchao Yao, Ya Zhang, Yanfeng Wang
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs). However, current works mainly pay attention to the exploration on single dataset of CXRs, which locks the potential of this powerful
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MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Jinduo Liu, Lu Han, Junzhong Ji
Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time dimension, which can describe the continuous neural activities in the brain. Recently, learning DEC from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data has attracted the attention of neuroinformatics researchers. However, the current methods fail to consider the gap between
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Histopathology Image Classification With Noisy Labels via The Ranking Margins IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Zhijie Wen, Haixia Wu, Shihui Ying
Clinically, histopathology images always offer a golden standard for disease diagnosis. With the development of artificial intelligence, digital histopathology significantly improves the efficiency of diagnosis. Nevertheless, noisy labels are inevitable in histopathology images, which lead to poor algorithm efficiency. Curriculum learning is one of the typical methods to solve such problems. However
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Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-25 Shouchang Guo, Jeffrey A. Fessler, Douglas C. Noll
Oscillating Steady-State Imaging (OSSI) is a recently developed fMRI acquisition method that can provide 2 to 3 times higher SNR than standard fMRI approaches. However, because the OSSI signal exhibits a nonlinear oscillation pattern, one must acquire and combine ${n}_{c}$ (e.g., 10) OSSI images to get an image that is free of oscillation for fMRI, and fully sampled acquisitions would compromise temporal
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Visible-Light Optical Coherence Tomography Fibergraphy of the Tree Shrew Retinal Ganglion Cell Axon Bundles IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-22 David A. Miller, Marta Grannonico, Mingna Liu, Elise Savier, Kara McHaney, Alev Erisir, Peter A. Netland, Jianhua Cang, Xiaorong Liu, Hao F. Zhang
We seek to develop techniques for high-resolution imaging of the tree shrew retina for visualizing and parameterizing retinal ganglion cell (RGC) axon bundles in vivo. We applied visible-light optical coherence tomography fibergraphy (vis-OCTF) and temporal speckle averaging (TSA) to visualize individual RGC axon bundles in the tree shrew retina. For the first time, we quantified individual RGC bundle
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Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-18 Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang
Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though underrepresented groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal
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A Technique to Quantify Very Low Activities in Regions of Interest With a Collimatorless Detector IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-18 Javier Caravaca, Kondapa Naidu Bobba, Shixian Du, Robin Peter, Grant T. Gullberg, Anil P. Bidkar, Robert R. Flavell, Youngho Seo
We present a new method to measure sub-microcurie activities of photon-emitting radionuclides in organs and lesions of small animals in vivo. Our technique, named the collimator-less likelihood fit, combines a very high sensitivity collimatorless detector with a Monte Carlo-based likelihood fit in order to estimate the activities in previously segmented regions of interest along with their uncertainties
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Score-based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-14 Ke Wang, Zicong Chen, Mingjia Zhu, Zhetao Li, Jian Weng, Tianlong Gu
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MP-Net: A Multi-Center Privacy-Preserving Network for Medical Image Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-13 Enjun Zhu, Haiyu Feng, Long Chen, Yongqiang Lai, Senchun Chai
In this paper, we present the Multi-Center Privacy-Preserving Network (MP-Net), a novel framework designed for secure medical image segmentation in multi-center collaborations. Our methodology offers a new approach to multi-center collaborative learning, capable of reducing the volume of data transmission and enhancing data privacy protection. Unlike federated learning, which requires the transmission
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3D Single Vessel Fractional Moving Blood Volume (3D-svFMBV): Fully Automated Tissue Perfusion Estimation Using Ultrasound IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-13 Yi Yin, Alys R. Clark, Sally L. Collins
Power Doppler ultrasound (PD-US) is the ideal modality to assess tissue perfusion as it is cheap, patient-friendly and does not require ionizing radiation. However, meaningful inter-patient comparison only occurs if differences in tissue-attenuation are corrected for. This can be done by standardizing the PD-US signal to a blood vessel assumed to have 100% vascularity. The original method to do this
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Quantification of Airway Structures by Persistent Homology IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-13 Shizuo Kaji, Naoya Tanabe, Tomoki Maetani, Yusuke Shiraishi, Ryo Sakamoto, Tsuyoshi Oguma, Katsuhiro Suzuki, Kunihiko Terada, Motonari Fukui, Shigeo Muro, Susumu Sato, Toyohiro Hirai
We propose two types of novel morphological metrics for quantifying the geometry of tubular structures on computed tomography (CT) images. We apply our metrics to identify irregularities in the airway of patients with chronic obstructive pulmonary disease (COPD) and demonstrate that they provide complementary information to the conventional metrics used to assess COPD, such as the tissue density distribution
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Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-13 Xiajun Jiang, Ryan Missel, Maryam Toloubidokhti, Karli Gillette, Anton J. Prassl, Gernot Plank, B. Milan Horáček, John L. Sapp, Linwei Wang
State-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data. In this paper, we present a novel hybrid SSM framework for electrocardiographic imaging
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Multi-channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-11 Weiwen Wu, Jiayi Pan, Yanyang Wang, Shaoyu Wang, Jianjia Zhang
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Correction for X-Ray Scatter and Detector Crosstalk in Dark-Field Radiography IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-07 Theresa Urban, Wolfgang Noichl, Klaus Juergen Engel, Thomas Koehler, Franz Pfeiffer
Dark-field radiography, a new X-ray imaging method, has recently been applied to human chest imaging for the first time. It employs conventional X-ray devices in combination with a Talbot-Lau interferometer with a large field of view, providing both attenuation and dark-field radiographs. It is well known that sample scatter creates artifacts in both modalities. Here, we demonstrate that also X-ray
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Multi-Grained Radiology Report Generation With Sentence-Level Image-Language Contrastive Learning IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-05 Aohan Liu, Yuchen Guo, Jun-Hai Yong, Feng Xu
The automatic generation of accurate radiology reports is of great clinical importance and has drawn growing research interest. However, it is still a challenging task due to the imbalance between normal and abnormal descriptions and the multi-sentence and multi-topic nature of radiology reports. These features result in significant challenges to generating accurate descriptions for medical images
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Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-04 Adam Schmidt, Omid Mohareri, Simon P. DiMaio, Septimiu E. Salcudean
Quantifying performance of methods for tracking and mapping tissue in endoscopic environments is essential for enabling image guidance and automation of medical interventions and surgery. Datasets developed so far either use rigid environments, visible markers, or require annotators to label salient points in videos after collection. These are respectively: not general, visible to algorithms, or costly
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3D Virtual Histopathology by Phase-Contrast X-Ray Micro-CT for Follicular Thyroid Neoplasms IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-04 Kiarash Tajbakhsh, Olga Stanowska, Antonia Neels, Aurel Perren, Robert Zboray
Histological analysis is the core of follicular thyroid carcinoma (FTC) classification. The histopathological criteria of capsular and vascular invasion define malignancy and aggressiveness of FTC. Analysis of multiple sections is cumbersome and as only a minute tissue fraction is analyzed during histopathology, under-sampling remains a problem. Application of an efficient tool for complete tissue
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Learning Robust Shape Regularization for Generalizable Medical Image Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-03-04 Kecheng Chen, Tiexin Qin, Victor Ho-Fun Lee, Hong Yan, Haoliang Li
Generalizable medical image segmentation enables models to generalize to unseen target domains under domain shift issues. Recent progress demonstrates that the shape of the segmentation objective, with its high consistency and robustness across domains, can serve as a reliable regularization to aid the model for better cross-domain performance, where existing methods typically seek a shared framework
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Landmark Localization From Medical Images With Generative Distribution Prior IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-29 Zixun Huang, Rui Zhao, Frank H. F. Leung, Sunetra Banerjee, Kin-Man Lam, Yong-Ping Zheng, Sai Ho Ling
In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization
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Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-23 Xiangxi Meng, Kaicong Sun, Jun Xu, Xuming He, Dinggang Shen
Synthesis of unavailable imaging modalities from available ones can generate modality-specific complementary information and enable multi-modality based medical images diagnosis or treatment. Existing generative methods for medical image synthesis are usually based on cross-modal translation between acquired and missing modalities. These methods are usually dedicated to specific missing modality and
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Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-23 Parisa Ghaderi Daneshmand, Hossein Rabbani
Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary
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Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-22 Zhenguo Wang, Yaping Wu, Zeheng Xia, Xinyi Chen, Xiaochen Li, Yan Bai, Yun Zhou, Dong Liang, Hairong Zheng, Yongfeng Yang, Shanshan Wang, Meiyun Wang, Tao Sun
Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was
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Pathological Priors Inspired Network for Vertebral Osteophytes Recognition IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-22 Junzhang Huang, Xiongfeng Zhu, Ziyang Chen, Guoye Lin, Meiyan Huang, Qianjin Feng
Automatic vertebral osteophyte recognition in Digital Radiography is of great importance for the early prediction of degenerative disease but is still a challenge because of the tiny size and high inter-class similarity between normal and osteophyte vertebrae. Meanwhile, common sampling strategies applied in Convolution Neural Network could cause detailed context loss. All of these could lead to an
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Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-22 Lifang Zhou, Yu Jiang, Weisheng Li, Jun Hu, Shenhai Zheng
The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and
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Multi-Scale Feature Alignment for Continual Learning of Unlabeled Domains IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-21 Kevin Thandiackal, Luigi Piccinelli, Rajarsi Gupta, Pushpak Pati, Orcun Goksel
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplines such as histopathology, this is crucial since large datasets with detailed annotations are scarce. While the majority of existing UDA methods focus on the adaptation from a labeled source to a single unlabeled target domain
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Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-21 Mengya Xu, Mobarakol Islam, Long Bai, Hongliang Ren
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time and exhibit catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after learning a new one. Specifically
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Randomizing Human Brain Function Representation for Brain Disease Diagnosis IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-20 Mengjun Liu, Huifeng Zhang, Mianxin Liu, Dongdong Chen, Zixu Zhuang, Xin Wang, Lichi Zhang, Daihui Peng, Qian Wang
Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective
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LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Muhammad F. A. Chaudhary, Sarah E. Gerard, Gary E. Christensen, Christopher B. Cooper, Joyce D. Schroeder, Eric A. Hoffman, Joseph M. Reinhardt
Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to
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Brain Image Segmentation for Ultrascale Neuron Reconstruction via an Adaptive Dual-Task Learning Network IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Min Liu, Shuhan Wu, Runze Chen, Zhuangdian Lin, Yaonan Wang, Erik Meijering
Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution
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A Collaborative Self-Supervised Domain Adaptation for Low-Quality Medical Image Enhancement IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Qingshan Hou, Yaqi Wang, Peng Cao, Shuai Cheng, Linqi Lan, Jinzhu Yang, Xiaoli Liu, Osmar R. Zaiane
Medical image analysis techniques have been employed in diagnosing and screening clinical diseases. However, both poor medical image quality and illumination style inconsistency increase uncertainty in clinical decision-making, potentially resulting in clinician misdiagnosis. The majority of current image enhancement methods primarily concentrate on enhancing medical image quality by leveraging high-quality
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Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Lei Li, Julia Camps, Zhinuo Jenny Wang, Marcel Beetz, Abhirup Banerjee, Blanca Rodriguez, Vicente Grau
Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring
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Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi
Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical
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Wavelet-Inspired Multi-channel Score-based Model for Limited-angle CT Reconstruction IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-19 Jianjia Zhang, Haiyang Mao, Xinran Wang, Yuan Guo, Weiwen Wu
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Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-14 Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem
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Simultaneous Activity and Attenuation Estimation in TOF-PET With TV-Constrained Nonconvex Optimization IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-14 Zhimei Ren, Emil Y. Sidky, Rina Foygel Barber, Chien-Min Kao, Xiaochuan Pan
An alternating direction method of multipliers (ADMM) framework is developed for nonsmooth biconvex optimization for inverse problems in imaging. In particular, the simultaneous estimation of activity and attenuation (SAA) problem in time-of-flight positron emission tomography (TOF-PET) has such a structure when maximum likelihood estimation (MLE) is employed. The ADMM framework is applied to MLE for
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A Dual Enrichment Synergistic Strategy to Handle Data Heterogeneity for Domain Incremental Cardiac Segmentation IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-12 Kang Li, Yu Zhu, Lequan Yu, Pheng-Ann Heng
Upon remarkable progress in cardiac image segmentation, contemporary studies dedicate to further upgrading model functionality toward perfection, through progressively exploring the sequentially delivered datasets over time by domain incremental learning. Existing works mainly concentrated on addressing the heterogeneous style variations, but overlooked the critical shape variations across domains
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Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction IEEE Trans. Med. Imaging (IF 8.9) Pub Date : 2024-02-09 Andrew P. Leynes, Nikhil Deveshwar, Srikantan S. Nagarajan, Peder E. Z. Larson
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations