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Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-15 Zeyu Gao,Chang Jia,Yang Li,Xianli Zhang,Bangyang Hong,Jialun Wu,Tieliang Gong,Chunbao Wang,Deyu Meng,Yefeng Zheng,Chen Li
Tissue segmentation is an essential task in computational pathology. However, relevant datasets for such a pixel-level classification task are hard to obtain due to the difficulty of annotation, bringing obstacles for training a deep learning-based segmentation model. Recently, contrastive learning has provided a feasible solution for mitigating the heavy reliance of deep learning models on annotation
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Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-14 Chao Liu,Deli Wang,Han Zhang,Wei Wu,Wenzhi Sun,Ting Zhao,Nenggan Zheng
Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morphologies. This has been a challenging step due to the variation and complexity of noisy intensity patterns in neuron images acquired from microscopes. Whereas
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Variational manifold learning from incomplete data: application to multi-slice dynamic MRI. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-11 Qing Zou,Abdul Haseeb Ahmed,Prashant Nagpal,Sarv Priya,Rolf F Schulte,Mathews Jacob
Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution MRI. We introduce a novel variational approach to learn a manifold from undersampled data
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TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-11 Alper Gungor,Baris Askin,Damla Alptekin Soydan,Emine Ulku Saritas,Can Baris Top,Tolga Cukur
Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account
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DuDoUFNet: Dual-domain under-to-fully-complete progressive restoration network for simultaneous metal artifact reduction and low-dose CT reconstruction. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-11 Bo Zhou,Xiongchao Chen,Huidong Xie,S Kevin Zhou,James S Duncan,Chi Liu
To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic
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Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for Histopathology Image Interpretable Classification. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-04 Mei Yang,Zhiying Xie,Zhaoxia Wang,Yun Yuan,Jue Zhang
Histopathology image classification plays a critical role in clinical diagnosis. However, due to the absence of clinical interpretability, most existing image-level classifiers remain impractical. To acquire the essential interpretability, lesion-level diagnosis is also provided, relying on detailed lesion-level annotations. Although the multiple-instance learning (MIL)-based approach can identify
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An Analytical Algorithm for Tensor Tomography from Projections Acquired about Three Axes. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-07-01 Weijie Tao,Damien Rohmer,Grant T Gullberg,Youngho Seo,Qiu Huang
Tensor fields are useful for modeling the structure of biological tissues. The challenge to measure tensor fields involves acquiring sufficient data of scalar measurements that are physically achievable and reconstructing tensors from as few projections as possible for efficient applications in medical imaging. In this paper, we present a filtered back-projection algorithm for the reconstruction of
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Multi-Center and Multi-Channel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-29 Xuegang Song,Feng Zhou,Alejandro F Frangi,Jiuwen Cao,Xiaohua Xiao,Yi Lei,Tianfu Wang,Baiying Lei
For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose
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A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-29 Dana Cohen Hochberg,Hayit Greenspan,Raja Giryes
The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled
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Anomaly Matters: An Anomaly-Oriented Model for Medical Visual Question Answering. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-29 Fuze Cong,Shibiao Xu,Li Guo,Yinbing Tian
Medical images contain various abnormal regions, most of which are closely related to the lesions or diseases. The abnormality or lesion is one of the major concerns during clinical practice and therefore becomes the key in answering questions about medical images. However, the recent efforts still focus on constructing a generic Visual Question Answering framework for medical-domain tasks, which is
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Intrinsic Temporal Performance of the RF Receive Coil in Magnetic Resonance Imaging. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-28 Yang Gao,Xiaotong Zhang
The functional magnetic resonance imaging (fMRI) at ultra-high field (UHF, ≥7T) is a powerful temporal acquisition method which promises to capture neuronal activities at submillimeter scale. But high-spatial-resolution fMRI still remains difficult, as the nuisance temporal noise which also grows with the main magnetic field strength. For decades, mainstream solutions in reducing motion-induced temporal
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Stacked Topological Preserving Dynamic Brain Networks Representation and Classification. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-27 Qi Zhu,Ruting Xu,Ran Wang,Xijia Xu,Zhiqiang Zhang,Daoqiang Zhang
In recent years, numerous studies have adopted rs-fMRI to construct dynamic functional connectivity networks (DFCNs) and applied them to the diagnosis of brain diseases, such as epilepsy and schizophrenia. Compared with the static brain networks, the DFCNs have a natural advantage in reflecting the process of brain activity due to the time information contained in it. However, most of the current methods
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Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-27 Xu Chen,Tianshu Kuang,Hannah Deng,Steve H Fung,Jaime Gateno,James J Xia,Pew-Thian Yap
Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net)
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Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-27 Ying Chen,Darui Jin,Bin Guo,Xiangzhi Bai
Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral
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Super-Resolution Photoacoustic Microscopy via Modified Phase Compounding. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-21 Mohammadreza Amjadian,Seyed Masood Mostafavi,Jiangbo Chen,Lidai Wang,Zhengtang Luo
Acoustic-resolution photoacoustic microscopy (AR-PAM) system can provide 3-D images of facial tissues. The lateral resolution of AR-PAM depends on the numerical aperture (NA) of the acoustic lens and the central frequency of the ultrasonic transducer. There is a trade-off between resolution enhancement and imaging depth. The acoustic beam is tight in the acoustic focal plane but expands in the out-of-focus
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Generative Consistency for Semi-Supervised Cerebrovascular Segmentation from TOF-MRA. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-21 Cheng Chen,Kangneng Zhou,Zhiliang Wang,Ruoxiu Xiao
Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional. In this paper, we propose a generative
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VQAMix: Conditional Triplet Mixup for Medical Visual Question Answering. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-21 Haifan Gong,Guanqi Chen,Mingzhi Mao,Zhen Li,Guanbin Li
Medical visual question answering (VQA) aims to correctly answer a clinical question related to a given medical image. Nevertheless, owing to the expensive manual annotations of medical data, the lack of labeled data limits the development of medical VQA. In this paper, we propose a simple yet effective data augmentation method, VQAMix, to mitigate the data limitation problem. Specifically, VQAMix
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3D US-based Evaluation and Optimization of Tumor Coverage for US-guided Percutaneous Liver Thermal Ablation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-20 Shuwei Xing,Joeana Cambranis Romero,Derek W Cool,Amol Mujoomdar,Elvis C S Chen,Terry M Peters,Aaron Fenster
Complete tumor coverage by the thermal ablation zone and with a safety margin (5 or 10 mm) is required to achieve the entire tumor eradication in liver tumor ablation procedures. However, 2D ultrasound (US) imaging has limitations in evaluating the tumor coverage by imaging only one or multiple planes, particularly for cases with multiple inserted applicators or irregular tumor shapes. In this paper
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Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-20 Mufeng Geng,Xiangxi Meng,Lei Zhu,Zhe Jiang,Mengdi Gao,Zhiyu Huang,Bin Qiu,Yicheng Hu,Yibao Zhang,Qiushi Ren,Yanye Lu
Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been
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Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-17 Jiaren Zou,Yue Cao
This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course
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First Dedicated Balloon Catheter for Magnetic Particle Imaging. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-16 Mandy Ahlborg,Thomas Friedrich,Thorsten Gottsche,Vincent Scheitenberger,Reinhard Linemann,Maximilian Wattenberg,Anne T Buessen,Tobias Knopp,Patryk Szwargulski,Michael G Kaul,Johannes Salamon,Thorsten M Buzug,Jorg Barkhausen,Franz Wegner
Vascular interventions are a promising application of Magnetic Particle Imaging enabling a high spatial and temporal resolution without using ionizing radiation. The possibility to visualize the vessels as well as the devices, especially at the same time using multi-contrast approaches, enables a higher accuracy for diagnosis and treatment of vascular diseases. Different techniques to make devices
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Multi-Modal Transformer for Accelerated MR Imaging. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-15 Chun-Mei Feng,Yunlu Yan,Geng Chen,Yong Xu,Ying Hu,Ling Shao,Huazhu Fu
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing
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CX-DaGAN: Domain Adaptation for Pneumonia Diagnosis on a Small Chest X-ray Dataset. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-10 Karen Sanchez,Carlos Hinojosa,Henry Arguello,Denis Kouame,Olivier Meyrignac,Adrian Basarab
Recent advances in deep learning led to several algorithms for the accurate diagnosis of pneumonia from chest X-rays. However, these models require large training medical datasets, which are sparse, isolated, and generally private. Furthermore, these models in medical imaging are known to over-fit to a particular data domain source, i.e., these algorithms do not conserve the same accuracy when tested
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MSDESIS: Multi-task stereo disparity estimation and surgical instrument segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-08 Dimitrios Psychogyios,Evangelos Mazomenos,Francisco Vasconcelos,Danail Stoyanov
Reconstructing the 3D geometry of the surgical site and detecting instruments within it are important tasks for surgical navigation systems and robotic surgery automation. Traditional approaches treat each problem in isolation and do not account for the intrinsic relationship between segmentation and stereo matching. In this paper, we present a learning-based framework that jointly estimates disparity
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Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-08 Kun Hu,Wenhua Wu,Wei Li,Milena Simic,Albert Zomaya,Zhiyong Wang
Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not
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Towards Robust Histology-Prior Embedding for Endomicroscopy Image Classification. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-06 Yun Gu,Yunze Xu,Xiaolin Huang,Jie Yang,Wei Xue,Guang-Zhong Yang
Representation learning is the critical task for medical image analysis in computer-aided diagnosis. However, it is challenging to learn discriminative features due to the limited size of the dataset and the lack of labels. In this paper, we propose a stochastic routing normalization and neighborhood embedding framework with application to breast tissue classification by learning discriminative features
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Benchmarking of deep architectures for segmentation of medical images. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-06 Daniel Gut,Zbislaw Tabor,Mateusz Szymkowski,Milosz Rozynek,Iwona Kucybala,Wadim Wojciechowski
In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using identical conditions to disentangle the influence of model architecture, model training, and parameter settings on the performance of a trained model.
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Table of Contents IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-01
Presents the table of contents for this issue of the publication.
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Augmented Equivariant Attention Networks for Microscopy Image Transformation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-06-01 Yaochen Xie,Yu Ding,Shuiwang Ji
It is time-consuming and expensive to take high-quality or high-resolution electron microscopy (EM) and fluorescence microscopy (FM) images. Taking these images could be even invasive to samples and may damage certain subtleties in the samples after long or intense exposures, often necessary for achieving high-quality or high-resolution in the first place. Advances in deep learning enable us to perform
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Semantic Graph Attention with Explicit Anatomical Association Modeling for Tooth Segmentation from CBCT Images. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-30 Pengcheng Li,Yang Liu,Zhiming Cui,Feng Yang,Yue Zhao,Chunfeng Lian,Chenqiang Gao
Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here
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Imaging of Single Transducer-Harmonic Motion Imaging-derived Displacements at Several Oscillation Frequencies Simultaneously. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-30 Md Murad Hossain,Elisa E Konofagou
Mapping of mechanical properties, dependent on the frequency of motion, is relevant in diagnosis, monitoring treatment response, or intra-operative surgical resection planning. While shear wave speeds at different frequencies have been described elsewhere, the effect of frequency on the "on-axis" acoustic radiation force (ARF)-induced displacement has not been previously investigated. Instead of generating
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SSIS-Seg: Simulation-supervised image synthesis for surgical instrument segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-27 Emanuele Colleoni,Dimitris Psychogyios,Beatrice Van Amsterdam,Francisco Vasconcelos,Danail Stoyanov
Surgical instrument segmentation can be used in a range of computer assisted interventions and automation in surgical robotics. While deep learning architectures have rapidly advanced the robustness and performance of segmentation models, most are still reliant on supervision and large quantities of labelled data. In this paper, we present a novel method for surgical image generation that can fuse
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Model-based quantitative elasticity reconstruction using ADMM. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-26 Shahed Mohammed,Mohammad Honarvar,Qi Zeng,Hoda Hashemi,Robert Rohling,Piotr Kozlowski,Septimiu Salcudean
We introduce two model-based iterative methods to obtain shear modulus images of tissue using magnetic resonance elastography. The first method jointly finds the displacement field that best fits tissue displacement data and the corresponding shear modulus. The displacement satisfies a viscoelastic wave equation constraint, discretized using the finite element method. Sparsifying regularization terms
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Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-25 Along He,Kai Wang,Tao Li,Wang Bo,Hong Kang,Huazhu Fu
Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction
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Anti-interference from Noisy Labels: Mean-Teacher-assisted Confident Learning for Medical Image Segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-23 Zhe Xu,Donghuan Lu,Jie Luo,Yixin Wang,Jiangpeng Yan,Kai Ma,Yefeng Zheng,Raymond Kai-Yu Tong
Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from
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Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-23 Yueming Jin,Yang Yu,Cheng Chen,Zixu Zhao,Pheng-Ann Heng,Danail Stoyanov
Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation
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Volumetric Fetal Flow Imaging with Magnetic Resonance Imaging. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-23 Datta Singh Goolaub,Jiawei Xu,Eric M Schrauben,Davide Marini,John C Kingdom,John G Sled,Mike Seed,Christopher K Macgowan
Fetal development relies on a complex circulatory network. Accurate assessment of flow distribution is important for understanding pathologies and potential therapies. In this paper, we demonstrate a method for volumetric imaging of fetal flow with magnetic resonance imaging (MRI). Fetal MRI faces challenges: small vascular structures, unpredictable motion, and inadequate traditional cardiac gating
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Perovskite CsPbBr3 single crystal detector for high flux X-ray photon counting. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-20 Lei Pan,Yihui He,Vladislav V Klepov,Michael C De Siena,Mercouri G Kanatzidis
X-ray photon-counting detectors capable of resolving the energies of single X-ray photons are critical in medical imaging, and a high count rate is essential for photon-counting detectors. Here, we report the performance of the perovskite CsPbBr3 single-crystal semiconductor detector for X-ray photon counting. The CsPbBr3 detector noise floor, energy response linearity, energy resolution, count rate
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Universal Real-Time Adaptive Signal Compression for High-Frame-Rate Optoacoustic Tomography. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-19 Ali Ozbek,Xose Luis Dean-Ben,Daniel Razansky
Optoacoustic tomography (OAT) has recently been advanced toward ultrafast volumetric imaging frame rates in the kilohertz range. As a result, excessive data processing and storage capacity requirements are increasingly being imposed on the imaging systems. OAT data commonly exhibit significant sparsity across the spatial, temporal or spectral domains, which facilitated the development of compressed
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Positronium Lifetime Image Reconstruction for TOF PET. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-18 Jinyi Qi,Bangyan Huang
Positron emission tomography is widely used in clinical and preclinical applications. Positronium lifetime carries information about the tissue microenvironment where positrons are emitted, but such information has not been captured because of two technical challenges. One challenge is the low sensitivity in detecting triple coincidence events. This problem has been mitigated by the recent developments
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Imaging Breast Microcalcifications Using Dark-Field Signal in Propagation-Based Phase-Contrast Tomography. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-18 A Aminzadeh,B D Arhatari,A Maksimenko,C J Hall,D Hausermann,A G Peele,J Fox,B Kumar,Z Prodanovic,M Dimmock,D Lockie,K M Pavlov,Y I Nesterets,D Thompson,S C Mayo,D M Paganin,S T Taba,S Lewis,P C Brennan,H M Quiney,T E Gureyev
Breast microcalcifications are an important primary radiological indicator of breast cancer. However, microcalcification classification and diagnosis may be still challenging for radiologists due to limitations of the standard 2D mammography technique, including spatial and contrast resolution. In this study, we propose an approach to improve the detection of microcalcifications in propagation-based
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Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-18 Wei Huang,Chang Chen,Zhiwei Xiong,Yueyi Zhang,Xuejin Chen,Xiaoyan Sun,Feng Wu
Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often expensive and time-consuming to collect due to dense distributions and complex structures of neurons. If the required quantity of manual annotations for learning
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PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-13 Xuzhe Zhang,Xinzi He,Jia Guo,Nabil Ettehadi,Natalie Aw,David Semanek,Jonathan Posner,Andrew Laine,Yun Wang
An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal
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Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-11 Yankun Lang,Chunfeng Lian,Deqiang Xiao,Hannah Deng,Kim-Han Thung,Peng Yuan,Jaime Gateno,Tianshu Kuang,David M Alfi,Li Wang,Dinggang Shen,James J Xia,Pew-Thian Yap
Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly
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Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-10 Ryoji Hirano,Takuto Emura,Otoichi Nakata,Toshiharu Nakashima,Miyako Asai,Kuriko Kagitani-Shimono,Haruhiko Kishima,Masayuki Hirata
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely
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Functional Brain Network Classification Based On Deep Graph Hashing Learning. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-09 Junzhong Ji,Yaqin Zhang
Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships
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Automated Radiographic Report Generation Purely On Transformer: A Multi-criteria Supervised Approach. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-04 Zhanyu Wang,Hongwei Han,Lei Wang,Xiu Li,Luping Zhou
Automated radiographic report generation is challenging in at least two aspects. First, medical images are very similar to each other and the visual differences of clinic importance are often fine-grained. Second, the disease-related words may be submerged by many similar sentences describing the common content of the images, causing the abnormal to be misinterpreted as the normal in the worst case
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Brain Connectivity based Graph Convolutional Networks for Infant Age Prediction. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-05-02 Yu Li,Xin Zhang,Jingxin Nie,Guowei Zhang,Ruiyan Fang,Xiangmin Xu,Zhengwang Wu,Dan Hu,Li Wang,Han Zhang,Weili Lin,Gang Li
Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional
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Lymph Node Metastasis Prediction from Whole Slide Images with Transformer-guided Multi-instance Learning and Knowledge Transfer. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-29 Zhihua Wang,Lequan Yu,Xin Ding,Xuehong Liao,Liansheng Wang
The gold standard for diagnosing lymph node metastasis of papillary thyroid carcinoma is to analyze the whole slide histopathological images (WSIs). Due to the large size of WSIs, recent computer-aided diagnosis approaches adopt the multi-instance learning (MIL) strategy and the key part is how to effectively aggregate the information of different instances (patches). In this paper, a novel transformer-guided
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Diffusion Kernel Attention Network for Brain Disorder Classification. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-26 Jianjia Zhang,Luping Zhou,Lei Wang,Mengting Liu,Dinggang Shen
Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its
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MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-25 David Zimmerer,Peter M Full,Fabian Isensee,Paul Jager,Tim Adler,Jens Petersen,Gregor Kohler,Tobias Ross,Annika Reinke,Antanas Kascenas,Bjorn Sand Jensen,Alison Q O'Neil,Jeremy Tan,Benjamin Hou,James Batten,Huaqi Qiu,Bernhard Kainz,Nina Shvetsova,Irina Fedulova,Dmitry V Dylov,Baolun Yu,Jianyang Zhai,Jingtao Hu,Runxuan Si,Sihang Zhou,Siqi Wang,Xinyang Li,Xuerun Chen,Yang Zhao,Sergio Naval Marimont,Giacomo
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution
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Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-22 Tingting Dan,Zhuobin Huang,Hongmin Cai,Paul J Laurienti,Guorong Wu
Functional connectivities (FC) of brain network manifest remarkable geometric patterns, which is the gateway to understanding brain dynamics. In this work, we present a novel geometric-attention neural network to characterize the time-evolving brain state change from the functional neuroimages by tracking the trajectory of functional dynamics on high-dimension Riemannian manifold of symmetric positive
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H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-21 Peixian Liang,Yizhe Zhang,Yifan Ding,Jianxu Chen,Chinedu S Madukoma,Tim Weninger,Joshua D Shrout,Danny Z Chen
Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extraction of rich instance information to facilitate good instance segmentation. While numerous efforts were put into developing new DL semantic segmentation models, less attention was paid to a key issue of how to effectively
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Functional parcellation of human brain using localized topo-connectivity mapping. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-20 Yu Zhao,Yurui Gao,Muwei Li,Adam W Anderson,Zhaohua Ding,John C Gore
The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM)
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Three-dimensional Deep-tissue Functional and Molecular Imaging by Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT). IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-20 Mucong Li,Nathan Beaumont,Chenshuo Ma,Juan Rojas,Tri Vu,Max Harlacher,Graeme O'Connell,Ryan C Gessner,Hailey Kilian,Ludmila Kasatkina,Yong Chen,Qiang Huang,Xiling Shen,Jonathan F Lovell,Vladislav V Verkhusha,Tomek Czernuszewicz,Junjie Yao
Non-invasive small-animal imaging technologies, such as optical imaging, magnetic resonance imaging and x-ray computed tomography, have enabled researchers to study normal biological phenomena or disease progression in their native conditions. However, existing small-animal imaging technologies often lack either the penetration capability for interrogating deep tissues (e.g., optical microscopy), or
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Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-20 Yakub A Bayhaqi,Arsham Hamidi,Ferda Canbaz,Alexander A Navarini,Philippe C Cattin,Azhar Zam
Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's
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CAR-Net: A Deep Learning-Based Deformation Model for 3D/2D Coronary Artery Registration. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-18 Wei Wu,Jingyang Zhang,Wenjia Peng,Hongzhi Xie,Shuyang Zhang,Lixu Gu
Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which hinders the intervention. This issue can be addressed by the deformable 3D/2D coronary artery registration technique, which fuses the pre-operative computed
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SPHARM-Net: Spherical Harmonics-based Convolution for Cortical Parcellation. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-18 Seungbo Ha,Ilwoo Lyu
We present a spherical harmonics-based convolutional neural network (CNN) for cortical parcellation, which we call SPHARM-Net. Recent advances in CNNs offer cortical parcellation on a fine-grained triangle mesh of the cortex. Yet, most CNNs designed for cortical parcellation employ spatial convolution that depends on extensive data augmentation and allows only predefined neighborhoods of specific spherical
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Dynamic Imaging using Deep Bi-linear Unsupervised Representation (DEBLUR). IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-18 Abdul Haseeb Ahmed,Qing Zou,Prashant Nagpal,Mathews Jacob
Bilinear models such as low-rank and dictionary methods, which decompose dynamic data to spatial and temporal factor matrices are powerful and memory-efficient tools for the recovery of dynamic MRI data. Current bilinear methods rely on sparsity and energy compaction priors on the factor matrices to regularize the recovery. Motivated by deep image prior, we introduce a novel bilinear model, whose factor
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Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC within 3 minutes. IEEE Trans. Med. Imaging (IF 11.037) Pub Date : 2022-04-18 Hongyan Liu,Oscar Van der Heide,Stefano Mandija,Cornelis A T Van den Berg,Alessandro Sbrizzi
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very