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Survival Prediction via Hierarchical Multimodal Co-Attention Transformer: A Computational Histology-Radiology Solution
The rapid advances in deep learning-based computational pathology and radiology have demonstrated the promise of using whole slide images (WSIs) and radiology images for survival prediction in cancer patients. However, most image-based survival prediction methods are limited to using either histology or radiology alone, leaving integrated approaches across histology and radiology relatively underdeveloped. There are two main challenges in integrating WSIs and radiology images: (1) the gigapixel nature of WSIs and (2) the vast difference in spatial scales between WSIs and radiology images. To address these challenges, in th...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning
Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cros...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Joint Reconstruction and Spectrum Refinement for Photon-Counting-Detector Spectral CT
Photon-counting detector CT (PCD-CT) is a revolutionary technology in decades in the field of CT. Its potential benefits in lowering noise, dose reduction, and material-specific imaging enable completely new clinical applications. Spectral reconstruction of basis material maps requires knowledge of the x-ray spectrum and the spectral response calibration of the detector. However, spectrum estimation errors caused by inaccurate energy threshold calibration will degrade the accuracy of the reconstructions. Existing spectrum estimation methods are not adequately modeled for bias in energy threshold position. Besides, directly...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

A 32-Channel Sleeve Antenna Receiver Array for Human Head MRI Applications at 10.5 T
For human brain magnetic resonance imaging (MRI), high channel count ( $ge 32$ ) radiofrequency receiver coil arrays are utilized to achieve maximum signal-to-noise ratio (SNR) and to accelerate parallel imaging techniques. With ultra-high field (UHF) MRI at 7 tesla (T) and higher, dipole antenna arrays have been shown to generate high SNR in the deep regions of the brain, however the array elements exhibit increased electromagnetic coupling with one another, making array construction more difficult with the increasing number of elements. Compared to a classical dipole antenna array, a sleeve antenna array incorporates the...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN)
Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of com...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Mitigating the Limited View Problem in Photoacoustic Tomography for a Planar Detection Geometry by Regularized Iterative Reconstruction
The use of a planar detection geometry in photoacoustic tomography results in the so- called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularization were evaluated using in-silico, experimental phantom, ex vivo and in vivo data. Compared to on...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Weakly Supervised Temporal Convolutional Networks for Fine-Grained Surgical Activity Recognition
Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervisio...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of inp...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Less Is More: Unsupervised Mask-Guided Annotated CT Image Synthesis With Minimum Manual Segmentations
As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use segmentation masks as synthesis conditions were proposed. However, these segmentation mask-conditioned generative models still relied on large, varied, and labeled training datasets, and they could only provide limited constraints on human anatomical structures, leadin...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers
Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival t...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

S3R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites
In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ( $text{S}^{{3}}text{R}$ ) method for explainable cross-site continual segmentation to m...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

FIT-Net: Feature Interaction Transformer Network for Pathologic Myopia Diagnosis
Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist physicians in diagnosing and grading pathological changes in pathologic myopia (PM). Clinically, due to the obvious differences in the position, shape, and size of the lesion structure in different scanning directions, ophthalmologists usually need to combine the lesion structure in the OCT images in the horizontal and vertical scanning directions to diagnose the type of pathological changes in PM. To address these challenges, we propose a novel feature interaction Transformer network (FIT-Net) to diagnose PM u...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation
Accurate segmentation of multiple abdominal organs from Computed Tomography (CT) images plays an important role in computer-aided diagnosis, treatment planning and follow-up. Currently, 3D Convolution Neural Networks (CNN) have achieved promising performance for automatic medical image segmentation tasks. However, most existing 3D CNNs have a large set of parameters and huge floating point operations (FLOPs), and 3D CT volumes have a large size, leading to high computational cost, which limits their clinical application. To tackle this issue, we propose a novel framework based on lightweight network and Knowledge Distillat...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution
This study develops a novel framework for minimum variance adaptive beamformers that uses a model data covariance learned from data using a sparse Bayesian learning algorithm (SBL-BF). The learned model data covariance effectively removes influence from correlated brain sources and is robust to noise and interference without the need for baseline measurements. A multiresolution framework for model data covariance computation and parallelization of the beamformer implementation enables efficient high-resolution reconstruction images. Results with both simulations and real datasets indicate that multiple highly correlated so...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs
Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a small amount of annotation is needed for generalisation to novel classes. This is especially seen in medical domains where dense pixel-level annotations are expensive to obtain. In this paper, we propose Regularized Prototypical Neural Ordinary Differential Equation (R-PNODE), a method that leverages intrinsic properties of Neural-ODEs, assisted and enhanced by additional cluster ...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research