IEEE transactions on medical imaging最新文献

In recent years, score-based diffusion models have emerged as effective tools for estimating score functions from empirical data distributions, particularly in integrating implicit priors with inverse problems like CT reconstruction. However, score-based diffusion models are rarely explored in challenging tasks such as metal artifact reduction (MAR). In this paper, we introduce the BiConstraints Diffusion Model for Metal Artifact Reduction (BCDMAR), an innovative approach that enhances iterative reconstruction with a conditional diffusion model for MAR. This method employs a metal artifact degradation operator in place of the traditional metal-excluded projection operator in the data-fidelity term, thereby preserving structure details around metal regions. However, scorebased diffusion models tend to be susceptible to grayscale shifts and unreliable structures, making it challenging to reach an optimal solution. To address this, we utilize a precorrected image as a prior constraint, guiding the generation of the score-based diffusion model. By iteratively applying the score-based diffusion model and the data-fidelity step in each sampling iteration, BCDMAR effectively maintains reliable tissue representation around metal regions and produces highly consistent structures in non-metal regions. Through extensive experiments focused on metal artifact reduction tasks, BCDMAR demonstrates superior performance over other state-of-the-art unsupervised and supervised methods, both quantitatively and in terms of visual results.

Accurate polyp segmentation plays a critical role from colonoscopy images in the diagnosis and treatment of colorectal cancer. While deep learning-based polyp segmentation models have made significant progress, they often suffer from performance degradation when applied to unseen target domain datasets collected from different imaging devices. To address this challenge, unsupervised domain adaptation (UDA) methods have gained attention by leveraging labeled source data and unlabeled target data to reduce the domain gap. However, existing UDA methods primarily focus on capturing class-wise representations, neglecting domain-wise representations. Additionally, uncertainty in pseudo labels could hinder the segmentation performance. To tackle these issues, we propose a novel Domain-interactive Contrastive Learning and Prototype-guided Self-training (DCL-PS) framework for cross-domain polyp segmentation. Specifically, domaininteractive contrastive learning (DCL) with a domain-mixed prototype updating strategy is proposed to discriminate class-wise feature representations across domains. Then, to enhance the feature extraction ability of the encoder, we present a contrastive learning-based cross-consistency training (CL-CCT) strategy, which is imposed on both the prototypes obtained by the outputs of the main decoder and perturbed auxiliary outputs. Furthermore, we propose a prototype-guided self-training (PS) strategy, which dynamically assigns a weight for each pixel during selftraining, filtering out unreliable pixels and improving the quality of pseudo-labels. Experimental results demonstrate the superiority of DCL-PS in improving polyp segmentation performance in the target domain. The code will be released at https://github.com/taozh2017/DCLPS.

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifact bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a unified DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code is publicly available at https://github.com/SiyuanYan1/PLDG/tree/main.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

Diagnosing malignant skin tumors accurately at an early stage can be challenging due to ambiguous and even confusing visual characteristics displayed by various categories of skin tumors. To improve diagnosis precision, all available clinical data from multiple sources, particularly clinical images, dermoscopy images, and medical history, could be considered. Aligning with clinical practice, we propose a novel Transformer model, named Remix-Former++ that consists of a clinical image branch, a dermoscopy image branch, and a metadata branch. Given the unique characteristics inherent in clinical and dermoscopy images, specialized attention strategies are adopted for each type. Clinical images are processed through a top-down architecture, capturing both localized lesion details and global contextual information. Conversely, dermoscopy images undergo a bottom-up processing with two-level hierarchical encoders, designed to pinpoint fine-grained structural and textural features. A dedicated metadata branch seamlessly integrates non-visual information by encoding relevant patient data. Fusing features from three branches substantially boosts disease classification accuracy. RemixFormer++ demonstrates exceptional performance on four single-modality datasets (PAD-UFES-20, ISIC 2017/2018/2019). Compared with the previous best method using a public multi-modal Derm7pt dataset, we achieved an absolute 5.3% increase in averaged F1 and 1.2% in accuracy for the classification of five skin tumors. Furthermore, using a large-scale in-house dataset of 10,351 patients with the twelve most common skin tumors, our method obtained an overall classification accuracy of 92.6%. These promising results, on par or better with the performance of 191 dermatologists through a comprehensive reader study, evidently imply the potential clinical usability of our method.

This study introduces a novel image reconstruction technique based on a diffusion model that is conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI (qMRI) reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.

The recent advent of in-context learning (ICL) capabilities in large pre-trained models has yielded significant advancements in the generalization of segmentation models. By supplying domain-specific image-mask pairs, the ICL model can be effectively guided to produce optimal segmentation outcomes, eliminating the necessity for model fine-tuning or interactive prompting. However, current existing ICL-based segmentation models exhibit significant limitations when applied to medical segmentation datasets with substantial diversity. To address this issue, we propose a dual similarity checkup approach to guarantee the effectiveness of selected in-context samples so that their guidance can be maximally leveraged during inference. We first employ large pre-trained vision models for extracting strong semantic representations from input images and constructing a feature embedding memory bank for semantic similarity checkup during inference. Assuring the similarity in the input semantic space, we then minimize the discrepancy in the mask appearance distribution between the support set and the estimated mask appearance prior through similarity-weighted sampling and augmentation. We validate our proposed dual similarity checkup approach on eight publicly available medical segmentation datasets, and extensive experimental results demonstrate that our proposed method significantly improves the performance metrics of existing ICL-based segmentation models, particularly when applied to medical image datasets characterized by substantial diversity.

In dental cone-beam computed tomography (CBCT), metal implants can cause metal artifacts, affecting image quality and the final medical diagnosis. To reduce the impact of metal artifacts, our proposed metal artifacts reduction (MAR) method takes a novel approach by integrating CBCT data with intraoral optical scanning data, utilizing information from these two different modalities to correct metal artifacts in the projection domain using a guided-diffusion model. The intraoral optical scanning data provides a more accurate generation domain for the diffusion model. We have proposed a multi-channel generation method in the training and generation stage of the diffusion model, considering the physical mechanism of CBCT, to ensure the consistency of the diffusion model generation. In this paper, we present experimental results that convincingly demonstrate the feasibility and efficacy of our approach, which introduces intraoral optical scanning data into the analysis and processing of projection domain data using the diffusion model for the first time, and modifies the diffusion model to better adapt to the physical model of CBCT.

The ability to recover tissue deformation from visual features is fundamental for many robotic surgery applications. This has been a long-standing research topic in computer vision, however, is still unsolved due to complex dynamics of soft tissues when being manipulated by surgical instruments. The ambiguous pixel correspondence caused by homogeneous texture makes achieving dense and accurate tissue tracking even more challenging. In this paper, we propose a novel self-supervised framework to recover tissue deformations from stereo surgical videos. Our approach integrates semantics, cross-frame motion flow, and long-range temporal dependencies to enable the recovered deformations to represent actual tissue dynamics. Moreover, we incorporate diffeomorphic mapping to regularize the warping field to be physically realistic. To comprehensively evaluate our method, we collected stereo surgical video clips containing three types of tissue manipulation (i.e., pushing, dissection and retraction) from two different types of surgeries (i.e., hemicolectomy and mesorectal excision). Our method has achieved impressive results in capturing deformation in 3D mesh, and generalized well across manipulations and surgeries. It also outperforms current state-of-the-art methods on non-rigid registration and optical flow estimation. To the best of our knowledge, this is the first work on self-supervised learning for dense tissue deformation modeling from stereo surgical videos. Our code will be released.