Journal of Medical Imaging最新文献

Purpose: Accurate airway measurement is critical for bronchitis quantification with computed tomography (CT), yet optimal protocols and the added value of photon-counting CT (PCCT) over energy-integrating CT (EICT) for reducing bias remain unclear. We quantified biomarker accuracy across modalities and protocols and assessed strategies to reduce bias.

Approach: A virtual imaging trial with 20 bronchitis anthropomorphic models was scanned using a validated simulator for two systems (EICT: SOMATOM Flash; PCCT: NAEOTOM Alpha) at 6.3 and 12.6 mGy. Reconstructions varied algorithm, kernel sharpness, slice thickness, and pixel size. Pi10 (square-root wall thickness at 10-mm perimeter) and WA% (wall-area percentage) were compared against ground-truth airway dimensions obtained from the 0.1-mm-precision anatomical models prior to CT simulation. External validation used clinical PCCT ( $n=22$ ) and EICT ( $n=80$ ).

Results: Simulated airway dimensions agreed with pathological references ( $R=0.89-0.93$ ). PCCT had lower errors than EICT across segmented generations ( $p<0.05$ ). Under optimal parameters, PCCT improved Pi10 and WA% accuracy by 26.3% and 64.9%. Across the tested PCCT and EICT imaging protocols, improvements were associated with sharper kernels (25.8% Pi10, 33.0% WA%), thinner slices (23.9% Pi10, 49.8% WA%), smaller pixels (17.0% Pi10, 23.1% WA%), and higher dose ( $\le 3.9\%$ ). Clinically, PCCT achieved higher maximum airway generation ( $8.8\pm 0.5$ versus $6.0\pm 1.1$ ) and lower variability, mirroring trends in virtual results.

Conclusions: PCCT improves the accuracy and consistency of airway biomarker quantification relative to EICT, particularly with optimized protocols. The validated virtual platform enables modality-bias assessment and protocol optimization for accurate, reproducible bronchitis measurements.

Purpose: Research in deep learning has shown a great advancement in the detection of melanoma. However, recent literature has emphasized a tendency of certain models to rely on disease-irrelevant visual artifacts such as dark corners, dense hair, or ruler marks. The dependence on these markers leads to biased models that do well for training but generalize poorly to heterogeneous clinical environments. To address these limitations in developing reliability in skin lesion detection, a lightweight cross-attention-based semantic dual (LCSD) transformer model was proposed.

Approach: The LCSD model extracts global-level semantic information, uses feature normalization to improve model accuracy, and employs semantic queries to improve domain generalization. Multihead attention is included with the semantic queries to refine global features. The cross-attention between feature maps and semantic query provides the model with a generalized encoding of the global context. The model improved the computational complexity from $O\left(n^{2}d\right)$ to $O(nmd+m^{2}d)$ , which makes the model suitable for the development of real-time and mobile applications.

Results: Empirical evaluation was conducted on three challenging datasets: Derm7pt-Dermoscopic, Derm7pt-Clinical, and PAD-UFES-20. The proposed model achieved classification accuracies of 82.82%, 72.95%, and 86.21%, respectively. These results demonstrate superior performance compared with conventional transformer-based models, highlighting both improved robustness and reduced computational cost.

Conclusion: The LCSD model mitigates the influence of irrelevant visual characteristics, enhances domain generalization, and ensures better adaptability across diverse clinical scenarios. Its lightweight design further supports deployment in mobile applications, making it a reliable and efficient solution for real-world melanoma detection.

Purpose: Colorectal cancer is the third most common cancer globally, with a high mortality rate due to metastatic progression, particularly in the liver. Surgical resection remains the main curative treatment, but only a small subset of patients is eligible for surgery at diagnosis. For patients with initially unresectable colorectal liver metastases (CRLM), neoadjuvant chemotherapy can downstage tumors, potentially making surgery feasible. We investigate whether radiomic signatures-quantitative imaging biomarkers derived from baseline computed tomography (CT) scans-can noninvasively predict chemotherapy response in patients with unresectable CRLM, offering a pathway toward personalized treatment planning.

Approach: We used radiomics combined with a stacking classifier (SC) to predict treatment outcome. Baseline CT imaging data from 355 patients with initially unresectable CRLM were analyzed using two regions of interest (ROIs) separately (all tumors in the liver and the largest tumor by volume). From each ROI, 107 radiomic features were extracted. The dataset was split into training and testing sets, and multiple machine learning models were trained and integrated via stacking to enhance prediction. Logistic regression coefficients were used to derive radiomic signatures.

Results: The SC achieved strong predictive performance, with an area under the receiver operating characteristic curve of up to 0.77 for response prediction. Logistic regression identified 12 and 7 predictive features for treatment response in all tumors and the largest tumor ROIs, respectively.

Conclusion: Our findings demonstrate that radiomic features from baseline CT scans can serve as robust, interpretable biomarkers for predicting chemotherapy response, offering insights to guide personalized treatment in unresectable CRLM.

Purpose: Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and high recurrence rates. Magnetic resonance imaging (MRI) is widely used for tumor assessment, but manual segmentation is labor-intensive and variable. Existing deep learning methods often lack generalizability, calibrated confidence, and robust uncertainty quantification.

Approach: We propose ER²Net, an evidential reasoning-enabled neural network for reliable TNBC tumor segmentation on MRI. ER²Net trains multiple U-Net variants with dropouts to generate diverse predictions and introduces pixel-wise reliability to quantify model agreement. We then introduce two ensemble fusion techniques: weighted reliability (WR) segmentation, which leverages pixel-wise reliability to enhance sensitivity, and Bayesian fusion (BF), which integrates predictions probabilistically for robust consensus. Confidence calibration is achieved using evidential reasoning, and we further propose pixel-wise reliable confidence entropy (PWRE) as a uncertainty measure.

Results: ER²Net improved performance compared with individual models. WR achieved IoU = 0.886, sensitivity = 0.928, precision = 0.952, and Hausdorff distance = 5.429 mm, whereas BF achieved IoU = 0.885 and sensitivity = 0.929. Reliable fusion provided the best calibration [expected calibration error = 0.00003; maximum calibration error = 0.017]. PWRE produced lower variance than conventional entropy, yielding more stable uncertainty estimates.

Conclusion: ER²Net introduces WR segmentation and BF as enhanced fusion techniques and PWRE as a uncertainty metric. Together, these advances improve segmentation accuracy, sensitivity, confidence calibration, and uncertainty estimation, paving the way for reliable MRI-based tools to support personalized treatment planning and response assessment in TNBC.

Purpose: Papillary thyroid carcinoma (PTC) is a common thyroid cancer, and accurate preoperative assessment of lateral cervical lymph node metastasis is critical for surgical planning. Current methods are often subjective and prone to misdiagnosis. This study aims to improve the accuracy of metastasis evaluation using a deep learning-based segmentation method on enhanced computed tomography (CT) images.

Approach: We propose a YOLOv8-based deep learning model integrated with a deformable self-attention module to enhance metastatic lymph node segmentation. The model was trained on a large dataset of pathology-confirmed CT images from PTC patients.

Results: The model demonstrated diagnostic performance comparable to experienced physicians, with high precision in identifying metastatic nodes. The deformable self-attention module improved segmentation accuracy, with strong sensitivity and specificity.

Conclusion: This deep learning approach improves the accuracy of preoperative assessment for lateral cervical lymph node metastasis in PTC patients, aiding surgical planning, reducing misdiagnosis, and lowering medical costs. It shows promise for enhancing patient outcomes in PTC management.

Purpose: Deep learning (DL) models have achieved promising performance in histologic whole-slide image analysis for various clinical applications. However, their black-box nature hinders the interpretability of DL features for clinical adoption. By contrast, hand-crafted features (HCFs) directly calculated from images offer strong interpretability but with reduced predictive power of DL models. The relationship between DL features and HCFs remains insufficiently explored. We aim to enhance the interpretability and performance of DL models using a weak-to-strong generalization (WSG) framework that integrates HCFs into the learning process.

Approach: The proposed WSG framework leverages an interpretable and HCF-based "weak" teacher model that supervises a "strong" DL student model to learn and improve itself by generalizing from weaker forms of reasoning to stronger ones, for classification tasks. An adaptive bootstrap WSG loss function is designed to optimize the transfer of knowledge from hand-crafted to deep-learned features, enabling systematic analysis of feature interactions. Innovatively, mutual information (MI) between HCFs and DL features learned by student models is analyzed to gain insights into their correlations and the interpretability of DL features. The framework is evaluated using extensive experiments on three public datasets with diverse combinations of teacher and student models for tumor classification.

Results: The WSG framework achieves consistent improvements in classification performance across all evaluated models. Qualitative saliency-map analysis indicates that WSG supervision enables student models to concentrate on diagnostically relevant regions, thereby improving interpretability. Furthermore, quantitative analysis reveals a notable increase in MI between hand-crafted and deep-learned features following WSG training compared with that without WSG training, indicating more effective integration of expert knowledge into the learned representations.

Conclusions: Our study elucidates the key HCFs that drive DL model predictions in histologic image classification. The findings demonstrate that integrating HCFs into DL model training via the WSG framework can enhance both the interpretability and the model's predictive performance, supporting their broader clinical adoption.

Purpose: Skin lesion segmentation plays a significant role in the diagnosis and treatment of skin cancer. Accurate skin lesion segmentation is essential for skin cancer diagnosis and treatment but is challenged by ambiguous boundaries and diverse lesion shapes and sizes. We aim to improve segmentation performance with enhanced boundary preservation.

Approach: We propose BAF-UNet, a boundary-aware segmentation network. It integrates a multiscale boundary-aware feature fusion (BFF) module to combine low-level boundary features with high-level semantic information, and a boundary-aware vision transformer (BAViT) that incorporates boundary guidance into MobileViT to capture local and global context. A boundary-focused loss function is also introduced to prioritize edge accuracy during training. The model is evaluated on ISIC2016, ISIC2017, and PH2 datasets.

Results: Experiments demonstrate that BAF-UNet improves Dice scores and boundary accuracy compared to baseline models. The BFF and BAViT modules enhance boundary delineation while maintaining robustness across lesions of varying shapes and sizes.

Conclusions: BAF-UNet effectively integrates boundary guidance into feature fusion and transformer-based context modeling, significantly improving segmentation accuracy, particularly along lesion edges, and shows potential for clinical application in automated skin cancer diagnosis.

Purpose: Dynamic focusing of received ultrasound signals, or beamforming, is foundational for ultrasound imaging. Conventionally, it requires arrays of ultrasound sensors to estimate where sound came from using time-of-flight (TOF) measurements. We demonstrate passive beamforming with a single biaxial sensor and accurate passive acoustic mapping with two biaxial sensors using only direction of arrival (DOA) information.

Approach: We introduce two single-element biaxial beamforming algorithms and four biaxial image reconstruction algorithms for a two-element biaxial piezoceramic transducer array. Imaging of a hemispherical acoustic source is characterized in an acoustic scanning tank within the region $-30.29\mathrm{mm}$ $\le x\le$ 29.94 mm and 50.11 mm $\le z\le$ 90.45 mm relative to the center of the array. Imaging performance is contrasted with delay, sum, and integrate (DSAI) and delay, multiply, sum, and integrate (DMSAI) algorithms.

Results: Single-element biaxial beamforming can identify DOA with a median error (± interquartile range) of $0.36\pm 0.63\mathrm{deg}$ and median full-width half-prominence of $7.3\pm 8.6\mathrm{deg}$ . Using both array elements, DOA-only images demonstrate overall median localization error of 6.41 mm (lateral: 1.02 mm, axial: 5.85 mm, signal-to-noise ratio (SNR): 15.37) and DOA + TOF images demonstrate overall median error of 6.91 mm (lateral: 1.69 mm, axial: 6.11 mm, SNR: 18.37).

Conclusions: To the best of our knowledge, we provide the first demonstration of single-element beamforming using a single stationary piezoceramic and the first demonstration of passive ultrasound imaging without the use of TOF information. These results enable simpler, smaller, more cost-effective arrays for passive ultrasound imaging.

Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning. Although multi-inversion time (multi-TI) $T_1$ -weighted ( $T_1$ -w) magnetic resonance (MR) imaging improves visualization, it is only acquired in specific clinical settings and not available in common public MR datasets.

Approach: We present SyMTIC (synthetic multi-TI contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired $T_1$ -w, $T_2$ -weighted ( $T_2$ -w), and fluid-attenuated inversion recovery (FLAIR) images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time ( $T1$ ) and proton density ( $\rho$ ) maps. These maps are then used to compute multi-TI images with arbitrary inversion times.

Results: SyMTIC was trained using paired magnetization prepared rapid acquisition with gradient echo (MPRAGE) and fast gray matter acquisition T1 inversion recovery (FGATIR) images along with $T_2$ -w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data. The synthetic images, especially for TI values between 400 to 800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei.

Conclusion: SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. When paired with the HACA3 algorithm, it generalizes well to varied clinical datasets, including those without FLAIR or $T_2$ -w images and unknown parameters, offering a practical solution for improving brain MR image visualization and analysis.