Journal of Medical Imaging最新文献

Purpose: Perceptual error is a significant cause of medical errors in radiology. Given the amount of information in a medical image, an image interpreter may become distracted by information unrelated to their search pattern. This may be especially challenging for novices. We aim to examine teaching medical trainees to evaluate chest radiographs (CXRs) for pulmonary nodules on limited field-of-view (LFOV) images, with the field of view (FOV) restricted to the lungs and mediastinum.

Approach: Healthcare trainees with limited exposure to interpreting images were asked to identify pulmonary nodules on CXRs, half of which contained nodules. The control and experimental groups evaluated two sets of CXRs. After the first set, the experimental group was trained to evaluate LFOV images, and both groups were again asked to assess CXRs for pulmonary nodules. Participants were given surveys after this educational session to determine their thoughts about the training and symptoms of computer vision syndrome (CVS).

Results: There was a significant improvement in performance in pulmonary nodule identification for both the experimental and control groups, but the improvement was more considerable in the experimental group ( $p\text{-}\text{value}=0.022$ ). Survey responses were uniformly positive, and each question was statistically significant (all $p\text{-}\text{values}<0.001$ ).

Conclusions: Our results show that using LFOV images may be helpful when teaching trainees specific high-yield perceptual tasks, such as nodule identification. The use of LFOV images was associated with reduced symptoms of CVS.

Purpose: Contrast-enhanced ultrasound (CEUS) is a reliable tool to diagnose focal liver lesions, which appear ambiguous in normal B-mode ultrasound. However, interpretation of the dynamic contrast sequences can be challenging, hindering the widespread application of CEUS. We investigate the use of a deep-learning-based image classifier for determining the diagnosis-relevant feature washout from CEUS acquisitions.

Approach: We introduce a data representation, which is agnostic to data heterogeneity regarding lesion size, subtype, and length of the sequences. Then, an image-based classifier is exploited for washout classification. Strategies to cope with sparse annotations and motion are systematically evaluated, as well as the potential benefits of using a perfusion model to cover missing time points.

Results: Results indicate decent performance comparable to studies found in the literature, with a maximum balanced accuracy of 84.0% on the validation and 82.0% on the test set. Correlation-based frame selection yielded improvements in classification performance, whereas further motion compensation did not show any benefit in the conducted experiments.

Conclusions: It is shown that deep-learning-based washout classification is feasible in principle. It offers a simple form of interpretability compared with benign versus malignant classifications. The concept of classifying individual features instead of the diagnosis itself could be extended to other features such as the arterial inflow behavior. The main factors distinguishing it from existing approaches are the data representation and task formulation, as well as a large dataset size with 500 liver lesions from two centers for algorithmic development and testing.

Purpose: We aim to investigate and isolate the distinctive acoustic properties generated by chemically crosslinked microbubble clusters (CCMCs) using machine learning (ML) techniques, specifically using an anomaly detection model based on autoencoders.

Approach: CCMCs were synthesized via copper-free click chemistry and subjected to acoustic analysis using a clinical transducer. Radiofrequency data were acquired, processed, and organized into training and testing datasets for the ML models. We trained an anomaly detection model with the nonclustered microbubbles (MBs) and tested the model on the CCMCs to isolate the unique acoustics. We also had a separate set of control experiments that was performed to validate the anomaly detection model.

Results: The anomaly detection model successfully identified frames exhibiting unique acoustic signatures associated with CCMCs. Frequency domain analysis further confirmed that these frames displayed higher amplitude and energy, suggesting the occurrence of potential coalescence events. The specificity of the model was validated through control experiments, in which both groups contained only individual MBs without clustering. As anticipated, no anomalies were detected in this control dataset, reinforcing the model's ability to distinguish clustered MBs from nonclustered ones.

Conclusions: We highlight the feasibility of detecting and distinguishing the unique acoustic characteristics of CCMCs, thereby improving the detectability and localization of contrast agents in ultrasound imaging. The elevated acoustic amplitudes produced by CCMCs offer potential advantages for more effective contrast agent detection, which is particularly valuable in super-resolution ultrasound imaging. Both the contrast agent and the ML-based analysis approach hold promise for a wide range of applications.

Purpose: 3D ultrasound delivers high-resolution, real-time images of soft tissues, which are essential for pain research. However, manually distinguishing various tissues for quantitative analysis is labor-intensive. We aimed to automate multilayer segmentation in 3D ultrasound volumes using minimal annotated data by developing generative reinforcement network plus (GRN+), a semi-supervised multi-model framework.

Approach: GRN+ integrates a ResNet-based generator and a U-Net segmentation model. Through a method called segmentation-guided enhancement (SGE), the generator produces new images under the guidance of the segmentation model, with its weights adjusted according to the segmentation loss gradient. To prevent gradient explosion and secure stable training, a two-stage backpropagation strategy was implemented: the first stage propagates the segmentation loss through both the generator and segmentation model, whereas the second stage concentrates on optimizing the segmentation model alone, thereby refining mask prediction using the generated images.

Results: Tested on 69 fully annotated 3D ultrasound scans from 29 subjects with six manually labeled tissue layers, GRN+ outperformed all other semi-supervised methods in terms of the Dice coefficient using only 5% labeled data, despite not using unlabeled data for unsupervised training. In addition, when applied to fully annotated datasets, GRN+ with SGE achieved a 2.16% higher Dice coefficient while incurring lower computational costs compared to other models.

Conclusions: GRN+ provides accurate tissue segmentation while reducing both computational expenses and the dependency on extensive annotations, making it an effective tool for 3D ultrasound analysis in patients with chronic lower back pain.

Purpose: Accurate iodine quantification in contrast-enhanced head CT is crucial for precise diagnosis and treatment planning. Traditional CT methods, which use energy-integrating detectors and dual-exposure techniques for material discrimination, often increase patient radiation exposure and are susceptible to motion artifacts and spectral resolution loss. Photon counting detectors (PCDs), capable of acquiring multiple energy windows in a single exposure with superior energy resolution, offer a promising alternative. However, the adoption of these technological advancements requires corresponding developments in evaluation methodologies to ensure their safe and effective implementation. One critical area of concern is the accuracy of iodine quantification, which is commonly assessed using cylindrical phantoms that neither replicate the shape of the human head nor incorporate skull-mimicking materials. These phantoms are widely used not only for testing but also for calibration, which may contribute to an overestimation of system performance in clinical applications. We address the impact of phantom design on evaluation metrics in spectral head CT, comparing conventional cylindrical phantoms to anatomically realistic elliptical phantoms with skull simulants.

Approach: We conducted simulations using a photon-counting spectral CT system equipped with cadmium telluride (CdTe) detectors, utilizing the Photon Counting Toolkit and Tigre CT software for detector response and CT geometry simulations. We compared cylindrical phantoms (20 cm diameter) to elliptical phantoms in three different sizes, incorporating skull materials with major/minor diameters and skull thicknesses of 18/14/0.5, 20/16/0.6, and 23/18/0.7 cm. Iodine inserts at concentrations of 0, 2, 5, and $10\mathrm{mg}/\mathrm{mL}$ with diameters of 1, 0.5, and 0.3 cm were used. We evaluated the influence of bowtie filters, various tube currents, and operating voltages. Image reconstruction was performed after beam hardening correction using the signal-to-thickness calibration (STC) method with standard filtered back projection, followed by both image-based and projection-based material decomposition.

Results: The results showed that image-based methods were more sensitive to phantom design, with cylindrical phantoms exhibiting enhanced performance compared with anatomically realistic designs across key metrics, including systematic error, root mean square error (RMSE), and precision. By contrast, the projection-based material decomposition method demonstrated greater consistency across different phantom designs and improved accuracy and precision. This highlights its potential for more reliable iodine quantification in complex geometries.

Conclusions: These findings underscore the critical importance of phantom design, especially the inclusion

Purpose: Histological analysis plays a crucial role in understanding tissue structure and pathology. Although recent advancements in registration methods have improved 2D histological analysis, they often struggle to preserve critical 3D spatial relationships, limiting their utility in both clinical and research applications. Specifically, constructing accurate 3D models from 2D slices remains challenging due to tissue deformation, sectioning artifacts, variability in imaging techniques, and inconsistent illumination. Deep learning-based registration methods have demonstrated improved performance but suffer from limited generalizability and require large-scale training data. In contrast, non-deep-learning approaches offer better generalizability but often compromise on accuracy.

Approach: We introduce ZeroReg3D, a zero-shot registration pipeline that integrates zero-shot deep learning-based keypoint matching and non-deep-learning registration techniques to effectively mitigate deformation and sectioning artifacts without requiring extensive training data.

Results: Comprehensive evaluations demonstrate that our pairwise 2D image registration method improves registration accuracy by $\sim 10\%$ over baseline methods, outperforming existing strategies in both accuracy and robustness. High-fidelity 3D reconstructions further validate the effectiveness of our approach, establishing ZeroReg3D as a reliable framework for precise 3D reconstruction from consecutive 2D histological images.

Conclusions: We introduced ZeroReg3D, a zero-shot registration pipeline tailored for accurate 3D reconstruction from serial histological sections. By combining zero-shot deep learning-based keypoint matching with optimization-based affine and non-rigid registration techniques, ZeroReg3D effectively addresses critical challenges such as tissue deformation, sectioning artifacts, staining variability, and inconsistent illumination without requiring retraining or fine-tuning.

The editorial discusses current JMI special sections/issues and calls for papers.

Purpose: Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. We aim to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information.

Approach: The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: (1) wavelet transformation for dimensionality reduction, (2) spectral cropping to eliminate low-intensity bands, and (3) scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32× compression while ensuring spectral fidelity and spatial feature retention.

Results: The wavelet-based method achieved up to 32× compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike principal component analysis and independent component analysis, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. In addition, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared with spectral binning.

Conclusions: We demonstrate that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information and therefore facilitates efficient data storage and processing, providing a way for the practical integration of HSI technology in clinical applications.

Purpose: The Response Evaluation Criteria in Solid Tumors (RECIST) relies solely on one-dimensional measurements to evaluate tumor response to treatments. However, thymic epithelial tumors (TETs), which frequently metastasize to the pleural cavity, exhibit a curvilinear morphology that complicates accurate measurement. To address this, we developed a physician-guided deep learning model and performed a retrospective study based on a patient cohort derived from clinical trials, aiming at efficient and reproducible volumetric assessments of TETs.

Approach: We used 231 computed tomography scans comprising 572 TETs from 81 patients. Tumors within the scans were identified and manually outlined to develop a ground truth that was used to measure model performance. TETs were characterized by their general location within the chest cavity: lung parenchyma, pleura, or mediastinum. Model performance was quantified on an unseen test set of 61 scans by mask Dice similarity coefficient (DSC), tumor DSC, absolute volume difference, and relative volume difference.

Results: We included 81 patients: 47 (58.0%) had thymic carcinoma; the remaining patients had thymoma B1, B2, B2/B3, or B3. The artificial intelligence (AI) model achieved an overall DSC of 0.77 per scan when provided with boxes surrounding the tumors as identified by physicians, corresponding to a mean absolute volume difference between the AI measurement and the ground truth of $16.1{\mathrm{cm}}^3$ and a mean relative volume difference of 22%.

Conclusion: We have successfully developed a robust annotation workflow and AI segmentation model for analyzing advanced TETs. The model has been integrated into the Picture Archiving and Communication System alongside RECIST measurements to enhance outcome assessments for patients with metastatic TETs.

Purpose: Medical image segmentation based on deep learning has played a crucial role in computer-aided medical diagnosis. However, they are still vulnerable to imperceptible adversarial attacks, which lead to potential misdiagnosis in clinical practice. Research on adversarial attack methods is beneficial for improving the robustness design of medical image segmentation models. Currently, there is a lack of research on adversarial attack methods toward deep learning-based medical image segmentation models. Existing attack methods often yield poor results in terms of both attack effects and image quality of adversarial examples and primarily focus on nontargeted attacks. To address these limitations and further investigate adversarial attacks on segmentation models, we propose an adversarial attack approach.

Approach: We propose an approach called momentum-driven adaptive feature-cosine-similarity with low-frequency constraint attack (MAFL-Attack). The proposed feature-cosine-similarity loss uses high-level abstract semantic information to interfere with the understanding of models about adversarial examples. The low-frequency component constraint ensures the imperceptibility of adversarial examples by constraining the low-frequency components. In addition, the momentum and dynamic step-size calculator are used to enhance the attack process.

Results: Experimental results demonstrate that MAFL-Attack generates adversarial examples with superior targeted attack effects compared with the existing Adaptive Segmentation Mask Attack method, in terms of the evaluation metrics of Intersection over Union, accuracy, $L_2$ , $L_{\infty }$ , Peak Signal to Noise Ratio, and Structure Similarity Index Measure.

Conclusions: The design idea of the MAFL-Attack inspires researchers to take corresponding defensive measures to strengthen the robustness of segmentation models.