IEE Transactions on Medical Imaging 
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.Multi-Source Domain Adaptation for Medical Image Segmentation
Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image seg...
Source: IEE Transactions on Medical Imaging - December 22, 2023 Category: Biomedical Engineering Source Type: research
AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging
Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this w...
Source: IEE Transactions on Medical Imaging - December 22, 2023 Category: Biomedical Engineering Source Type: research
MT4MTL-KD: A Multi-Teacher Knowledge Distillation Framework for Triplet Recognition
The recognition of surgical triplets plays a critical role in the practical application of surgical videos. It involves the sub-tasks of recognizing instruments, verbs, and targets, while establishing precise associations between them. Existing methods face two significant challenges in triplet recognition: 1) the imbalanced class distribution of surgical triplets may lead to spurious task association learning, and 2) the feature extractors cannot reconcile local and global context modeling. To overcome these challenges, this paper presents a novel multi-teacher knowledge distillation framework for multi-task triplet learn...
Source: IEE Transactions on Medical Imaging - December 21, 2023 Category: Biomedical Engineering Source Type: research
In Vivo Microwave-Induced Thermoacoustic Endoscopy for Colorectal Tumor Detection in Deep Tissue
Optical endoscopy, as one of the common clinical diagnostic modalities, provides irreplaceable advantages in the diagnosis and treatment of internal organs. However, the approach is limited to the characterization of superficial tissues due to the strong optical scattering properties of tissue. In this work, a microwave-induced thermoacoustic (TA) endoscope (MTAE) was developed and evaluated. The MTAE system integrated a homemade monopole sleeve antenna (diameter = 7 mm) for providing homogenized pulsed microwave irradiation to induce a TA signal in the colorectal cavity and a side-viewing focus ultrasonic transducer (diam...
Source: IEE Transactions on Medical Imaging - December 19, 2023 Category: Biomedical Engineering Source Type: research
Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals’ cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in ...
Source: IEE Transactions on Medical Imaging - December 18, 2023 Category: Biomedical Engineering Source Type: research
Passive Elastography for Clinical HIFU Lesion Detection
High-intensity Focused Ultrasound (HIFU) is a promising treatment modality for a wide range of pathologies including prostate cancer. However, the lack of a reliable ultrasound-based monitoring technique limits its clinical use. Ultrasound currently provides real-time HIFU planning, but its use for monitoring is usually limited to detecting the backscatter increase resulting from chaotic bubble appearance. HIFU has been shown to generate stiffening in various tissues, so elastography is an interesting lead for ablation monitoring. However, the standard techniques usually require the generation of a controlled push which ca...
Source: IEE Transactions on Medical Imaging - December 18, 2023 Category: Biomedical Engineering Source Type: research
Windowed Radon Transform for Robust Speed-of-Sound Imaging With Pulse-Echo Ultrasound
In recent years, methods estimating the spatial distribution of tissue speed of sound with pulse-echo ultrasound are gaining considerable traction. They can address limitations of B-mode imaging, for instance in diagnosing fatty liver diseases. Current state-of-the-art methods relate the tissue speed of sound to local echo shifts computed between images that are beamformed using restricted transmit and receive apertures. However, the aperture limitation affects the robustness of phase-shift estimations and, consequently, the accuracy of reconstructed speed-of-sound maps. Here, we propose a method based on the Radon transfo...
Source: IEE Transactions on Medical Imaging - December 18, 2023 Category: Biomedical Engineering Source Type: research
OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising
The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and compleme...
Source: IEE Transactions on Medical Imaging - December 14, 2023 Category: Biomedical Engineering Source Type: research
IMJENSE: Scan-Specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil ...
Source: IEE Transactions on Medical Imaging - December 13, 2023 Category: Biomedical Engineering Source Type: research
Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis
Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the...
Source: IEE Transactions on Medical Imaging - December 12, 2023 Category: Biomedical Engineering Source Type: research
SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images
Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (...
Source: IEE Transactions on Medical Imaging - December 12, 2023 Category: Biomedical Engineering Source Type: research
DeepTree: Pathological Image Classification Through Imitating Tree-Like Strategies of Pathologists
Digitization of pathological slides has promoted the research of computer-aided diagnosis, in which artificial intelligence analysis of pathological images deserves attention. Appropriate deep learning techniques in natural images have been extended to computational pathology. Still, they seldom take into account prior knowledge in pathology, especially the analysis process of lesion morphology by pathologists. Inspired by the diagnosis decision of pathologists, we design a novel deep learning architecture based on tree-like strategies called DeepTree. It imitates pathological diagnosis methods, designed as a binary tree s...
Source: IEE Transactions on Medical Imaging - December 12, 2023 Category: Biomedical Engineering Source Type: research
DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D m...
Source: IEE Transactions on Medical Imaging - December 8, 2023 Category: Biomedical Engineering Source Type: research
An Energy Matching Vessel Segmentation Framework in 3-D Medical Images
Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical scans is crucial for clinicians to visualize complex vasculature and diagnose related vascular diseases. However, a reliable and scalable vessel segmentation framework for HR3D scans remains a challenge. In this work, we propose a High-resolution Energy-matching Segmentation (HrEmS) framework that utilizes deep learning to directly process the entire HR3D scan and segment the vasculature to the finest level. The HrEmS framework introduces two novel components. Firstly, it uses the real-order total variation operator to construct a new loss fun...
Source: IEE Transactions on Medical Imaging - December 4, 2023 Category: Biomedical Engineering Source Type: research
A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images
Aortic segmentation from computed tomography (CT) is crucial for facilitating aortic intervention, as it enables clinicians to visualize aortic anatomy for diagnosis and measurement. However, aortic segmentation faces the challenge of variable geometry in space, as the geometric diversity of different diseases and the geometric transformations that occur between raw and measured images. Existing constraint-based methods can potentially solve the challenge, but they are hindered by two key issues: inaccurate definition of properties and inappropriate topology of transformation in space. In this paper, we propose a deformabl...
Source: IEE Transactions on Medical Imaging - December 4, 2023 Category: Biomedical Engineering Source Type: research
