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.

Construction of a Nearly Unbiased Statistical Estimator of Sinogram to Address CT Number Bias Issues in Low-Dose Photon Counting CT
Photon counting detector (PCD)-CT has demonstrated promise to reduce ionizing radiation exposure further and improve spatial resolution. However, when the radiation exposure or the detector pixel size is reduced, image noise is elevated, and the CT number becomes more inaccurate. This exposure level-dependent CT number inaccuracy is referred to as statistical bias. The issue of CT number statistical bias is rooted in the stochastic nature of the detected photon number, N, and a log transformation used to generate the sinogram projection data. Due to the nonlinear nature of the log transform, the statistical mean of the log...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain
In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnosis. The CADxDE includes material identification and machine learning (ML) based CADx. Benefits from DECT’s capability of performing virtual monoenergetic imaging with the identified materials, the responses of different tissue types (e.g., muscle, water, and fat) in lesions at each energy can be explored by ML for CADx. Without losing essential factors in the DECT scan, ...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

List-Mode PET Image Reconstruction Using Deep Image Prior
In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior (DIP) which is the first trial to integrate list-mode PET image reconstruction and CNN. The proposed list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates the regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP) using an alternating direction method of multipliers. We evaluated LM-DIPRecon using both simulation and clinical data, and it achieved sharper images and better tradeoff curves between con...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Bayesian Collaborative Learning for Whole-Slide Image Classification
Whole-slide image (WSI) classification is fundamental to computational pathology, which is challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc. Multiple instance learning (MIL) provides a promising way towards WSI classification, which nevertheless suffers from the memory bottleneck issue inherently, due to the gigapixel high resolution. To avoid this issue, the overwhelming majority of existing approaches have to decouple the feature encoder and the MIL aggregator in MIL networks, which may largely degrade the performance. Towards this end, this paper presents a Bayesian Collaborativ...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective ...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

PET Image Reconstruction With Kernel and Kernel Space Composite Regularizer
Led by the kernelized expectation maximization (KEM) method, the kernelized maximum-likelihood (ML) expectation maximization (EM) methods have recently gained prominence in PET image reconstruction, outperforming many previous state-of-the-art methods. But they are not immune to the problems of non-kernelized MLEM methods in potentially large reconstruction variance and high sensitivity to iteration numbers, and the difficulty in preserving image details and suppressing image variance simultaneously. To solve these problems, this paper derives, using the ideas of data manifold and graph regularization, a novel regularized ...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation
Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifica...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Partial Unbalanced Feature Transport for Cross-Modality Cardiac Image Segmentation
Deep learning based approaches have achieved great success on the automatic cardiac image segmentation task. However, the achieved segmentation performance remains limited due to the significant difference across image domains, which is referred to as domain shift. Unsupervised domain adaptation (UDA), as a promising method to mitigate this effect, trains a model to reduce the domain discrepancy between the source (with labels) and the target (without labels) domains in a common latent feature space. In this work, we propose a novel framework, named Partial Unbalanced Feature Transport (PUFT), for cross-modality cardiac im...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Truncated Normal Mixture Prior Based Deep Latent Model for Color Normalization of Histology Images
The variation in color appearance among the Hematoxylin and Eosin (H&E) stained histological images is one of the major problems, as the color disagreement may affect the computer aided diagnosis of histology slides. In this regard, the paper introduces a new deep generative model to reduce the color variation present among the histological images. The proposed model assumes that the latent color appearance information, extracted through a color appearance encoder, and stain bound information, extracted via stain density encoder, are independent of each other. In order to capture the disentangled color appearance an...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XB...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations
Convolutional neural networks (CNNs) have made enormous progress in medical image segmentation. The learning of CNNs is dependent on a large amount of training data with fine annotations. The workload of data labeling can be significantly relieved via collecting imperfect annotations which only match the underlying ground truths coarsely. However, label noises which are systematically introduced by the annotation protocols, severely hinders the learning of CNN-based segmentation models. Hence, we devise a novel collaborative learning framework in which two segmentation models cooperate to combat label noises in coarse anno...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI
Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images
In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing wi...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

X-Ray Dark-Field and Phase Retrieval Without Optics, via the Fokker–Planck Equation
Emerging methods of x-ray imaging that capture phase and dark-field effects are equipping medicine with complementary sensitivity to conventional radiography. These methods are being applied over a wide range of scales, from virtual histology to clinical chest imaging, and typically require the introduction of optics such as gratings. Here, we consider extracting x-ray phase and dark-field signals from bright-field images collected using nothing more than a coherent x-ray source and a detector. Our approach is based on the Fokker–Planck equation for paraxial imaging, which is the diffusive generalization of the tran...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Feature Masking on Non-Overlapping Regions for Detecting Dense Cells in Blood Smear Image
Detecting cells in blood smear images is of great significance for automatic diagnosis of blood diseases. However, this task is rather challenging, mainly because there are dense cells that are often overlapping, making some of the occluded boundary parts invisible. In this paper, we propose a generic and effective detection framework that exploits non-overlapping regions (NOR) for providing discriminative and confident information to compensate the intensity deficiency. In particular, we propose a feature masking (FM) to exploit the NOR mask generated from the original annotation information, which can guide the network t...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research