Journal of Medical Imaging最新文献

{"title":" Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease.","authors":"Jisoo Lee, Michael R Harowicz, Yuwen Chen, Hanxue Gu, Isaac S Alderete, Lin Li, Maciej A Mazurowski, Matthew G Hartwig","doi":"10.1117/1.JMI.13.3.034001","DOIUrl":"https://doi.org/10.1117/1.JMI.13.3.034001","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluates publicly available deep-learning-based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung sides, and to identify limitations impacting their use in preoperative planning in lung transplantation.</p><p><strong>Approach: </strong>This retrospective study included 32 patients who underwent chest CT scans at Duke University Health System between 2017 and 2019 (a total of 3645 2D axial slices). Patients with standard axial CT scans were selected based on the presence of two or more lung pathologies of varying severity. Lung segmentation was performed using three previously developed deep learning models: Unet-R231, TotalSegmentator, and MedSAM. Performance was assessed using quantitative metrics (volumetric similarity, Dice similarity coefficient, and Hausdorff distance) and a qualitative measure (four-point clinical acceptability scale).</p><p><strong>Results: </strong>Unet-R231 consistently outperformed TotalSegmentator and MedSAM in general for different severity levels and pathology categories ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). All models showed significant performance declines from mild to moderate-to-severe cases, particularly in volumetric similarity ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ), without significant differences among lung sides or pathology types.</p><p><strong>Conclusions: </strong>Unet-R231 provided the most accurate automated lung segmentation among evaluated models, with TotalSegmentator being a close second, although their performance declined significantly in moderate-to-severe cases, emphasizing the need for specialized model fine-tuning in severe pathology contexts.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 3","pages":"034001"},"PeriodicalIF":2.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13275165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and reliability of artery-vein differentiation in small-field macular OCT angiography.","authors":"Haneen Alfauri, Tugce Ilayda Turer, Cyriac Manjaly, Aditya Santoki, Senyue Hao, Marin Woronets, Chao Zhou, Rithwick Rajagopal","doi":"10.1117/1.JMI.13.2.025501","DOIUrl":"10.1117/1.JMI.13.2.025501","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate artery-vein (AV) differentiation in small-field macular optical coherence tomography angiography (OCTA) remains challenging due to a lack of standardized guidelines. We propose and validate criteria for <math><mrow><mn>3</mn> <mo>×</mo> <mn>3</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> ( <math><mrow><mn>10</mn> <mtext> </mtext> <mi>deg</mi> <mo>×</mo> <mtext> </mtext> <mn>10</mn> <mtext> </mtext> <mi>deg</mi></mrow> </math> on Spectralis; <math><mrow><mo>∼</mo> <mn>2.9</mn> <mo>×</mo> <mn>2.9</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> ) macular scans.</p><p><strong>Approach: </strong>Small field-of-view (FOV) OCTA scans were analyzed using established AV criteria for large-field ( <math><mrow><mn>12</mn> <mo>×</mo> <mn>12</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> ) OCTA, as applied by two masked readers and validated against color fundus photographs (CFPs) and near-infrared reflectance (NIR) images. Accuracy and reliability (Cohen's <math><mrow><mi>κ</mi></mrow> </math> ) were assessed. Pixel-level AV masks were annotated with a standardized threshold. Vessel diameters and intensities were compared within our dataset and in the publicly available OCTA-500 dataset to assess whether intrinsic vessel features support AV differentiation.</p><p><strong>Results: </strong>A total of 465 vessels from 20 healthy eyes were evaluated across 3 pseudo-branching orders using the criteria for OCTA. Annotators achieved high accuracy (95.1%, 92.3%) and strong intra/inter-rater reliability ( <math><mrow><mi>κ</mi> <mo>=</mo> <mn>0.84</mn></mrow> </math> ) with similarly high AV classification accuracy within pseudo-third-order vessels (97.15%). No significant AV diameter differences were observed in either dataset ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.261</mn></mrow> </math> and 0.442). The mean intensity was similar in our dataset ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.277</mn></mrow> </math> ; <math><mrow><mo>|</mo> <mi>Δ</mi> <mo>|</mo> <mo>=</mo> <mn>3.28</mn></mrow> </math> , 1.45% relative difference) but higher for veins in OCTA-500 ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.0001</mn></mrow> </math> ; <math><mrow><mo>|</mo> <mi>Δ</mi> <mo>|</mo> <mo>=</mo> <mn>3.42</mn></mrow> </math> , 1.63% relative difference).</p><p><strong>Conclusions: </strong>Accurate and reproducible AV labeling is feasible in <math><mrow><mn>3</mn> <mo>×</mo> <mn>3</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> scans, with strong inter- and intra-rater agreement. Vessel diameter and intensity add limited value. NIR-based alignment of OCTA with CFP provides reliable ground truth, supporting consistent manual labeling and OCTA segmentation.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"025501"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of 2D and 3D carotid plaque analysis and longitudinal <i>in vivo</i> ultrasound registration using 3D histology.","authors":"Yurim Lee, Maxwell J Kiernan, Carol C Mitchell, Shahriar Salamat, Stephanie M Wilbrand, Robert J Dempsey, Tomy Varghese","doi":"10.1117/1.JMI.13.2.027501","DOIUrl":"10.1117/1.JMI.13.2.027501","url":null,"abstract":"<p><strong>Purpose: </strong>Characterizing carotid plaque specimens based on two-dimensional (2D) \"representative\" histology sections is considered standard practice in clinics. In comparison, three-dimensional (3D) histology has the potential to provide much more useful, volumetric information about carotid plaques. Despite this, due to the increased requirement for manual labor, 3D histology has been less employed for carotid plaque characterization. Evaluating the representativeness of 2D histology and exploring clinical applications of 3D carotid plaque histology, particularly in the arena of registration to and correlations with <i>in vivo</i> ultrasound, could be insightful.</p><p><strong>Approach: </strong>Using 3D carotid plaque histology models, we evaluated the representativeness of 2D histology by comparing the predicted specimen composition based on 2D histology to the actual specimen composition based on 3D histology. We introduced a workflow that properly orients 3D carotid plaque histology based on transverse ultrasound and takes virtual histology slices at an angle to register histology to the longitudinal ultrasound. We correlated 3D histology composition to <i>in vivo</i> ultrasound parameters such as strain and grayscale features.</p><p><strong>Results: </strong>The 2D histology successfully predicted specimen composition (to within 3%) for 11 specimens out of 34. The 2D representative slice predictions generally overestimated calcification for more calcified specimens ( <math><mrow><mo>≳</mo> <mn>30</mn> <mo>%</mo></mrow> </math> calcified). Using 3D histology, we registered virtual histology to <i>in vivo</i> longitudinal ultrasound B-mode and strain. For B-mode, the registrations had higher IoU with respect to the ultrasonographer's annotations ( <math><mrow><mn>0.54</mn> <mo>±</mo> <mn>0.05</mn></mrow> </math> ) compared with the registrations with conventional 2D histology ( <math><mrow><mn>0.30</mn> <mo>±</mo> <mn>0.08</mn></mrow> </math> ). 3D histology composition was loosely related to all strain indices and grayscale features used in the study. In one of the cases, we note that hemorrhage corresponded to opposing strains.</p><p><strong>Conclusions: </strong>3D histology can be helpful for carotid plaque characterization as it enables a better understanding of plaque composition and better histology to <i>in vivo</i> ultrasound imaging registration.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"027501"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenging Hounsfield Unit cutoffs: spectral thresholding for synthetic coronary plaque phantoms on photon-counting CT.","authors":"Florian Goldmann, Michael Wels, Thomas Allmendinger, Manuela Goldmann, Ralf Gutjahr, Markus Jürgens, Jonas Neumann, Leonhard Rist, Karl Stierstorfer, Michael Sühling, Andreas Maier","doi":"10.1117/1.JMI.13.2.024003","DOIUrl":"10.1117/1.JMI.13.2.024003","url":null,"abstract":"<p><strong>Purpose: </strong>Assess whether photon-counting computed tomography (PCCT) improves discrimination of vulnerable coronary soft-plaque components by extending one-dimensional Hounsfield Unit (HU) thresholding to a simple, interpretable two-dimensional linear rule.</p><p><strong>Approach: </strong>We generated a synthetic cohort of <math><mrow><mi>N</mi> <mo>=</mo> <mn>225</mn></mrow> </math> coronary plaque phantoms with randomized anatomy, tissue composition (lipid-rich, fibrotic, calcified), and iodine concentrations. Ultra-high-resolution PCCT data were reconstructed into polychromatic T3D, high energy threshold, material-specific, and virtual monoenergetic images (VMIs). Voxel-wise logistic regression implemented single-image (1D) and dual-image (2D) decision rules; performance was assessed by the area under the receiver operating characteristic curve (ROC-AUC). Partial-volume behavior was quantified as correctness versus Euclidean distance to the nearest out-of-class voxel using isotonic regression with a phantom-level bootstrap.</p><p><strong>Results: </strong>Combining T3D with low-keV VMI yielded the best separation of lipid-rich and fibrous soft-plaque subtypes. A 2D linear rule on T3D + <math> <mrow><msub><mi>VMI</mi> <mn>50</mn></msub> </mrow> </math> achieved <math><mrow><mi>AUC</mi> <mo>=</mo> <mn>0.925</mn></mrow> </math> (95% CI: [0.912, 0.937]), exceeding 1D thresholding on T3D ( <math><mrow><mi>AUC</mi> <mo>=</mo> <mn>0.850</mn></mrow> </math> ; 95% CI: [0.821, 0.875]) and on <math> <mrow><msub><mi>VMI</mi> <mn>50</mn></msub> </mrow> </math> ( <math><mrow><mi>AUC</mi> <mo>=</mo> <mn>0.814</mn></mrow> </math> ; 95% CI: [0.780, 0.843]). Correctness increased with distance to the nearest out-of-class voxel and was <math><mrow><mo>≥</mo> <mn>95</mn> <mo>%</mo></mrow> </math> for voxels at distances <math><mrow><mi>D</mi> <mo>≥</mo> <mn>0.28</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> (lipid-rich) and <math><mrow><mi>D</mi> <mo>≥</mo> <mn>0.43</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> (fibrous) (lower 95% CI bounds: 0.20 and 0.41 mm). Accuracy degraded below these thresholds.</p><p><strong>Conclusions: </strong>A transparent, affine 2D threshold that combines routinely reconstructed PCCT images improves voxel-wise discrimination of lipid-rich versus fibrous plaque over conventional HU binning, yielding higher AUCs with tighter 95% confidence intervals. The derived boundary-distance guidance indicates where voxel-level decisions remain reliable, supporting interpretable, clinically pragmatic plaque assessment.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"024003"},"PeriodicalIF":1.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filter2Noise: a framework for interpretable and zero-shot low-dose CT image denoising.","authors":"Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei, Jinhua Wang, Ge Hu, Mingxuan Gu, Chengze Ye, Fabian Wagner, Lan Song, Siming Bayer, Andreas Maier","doi":"10.1117/1.JMI.13.2.024004","DOIUrl":"10.1117/1.JMI.13.2.024004","url":null,"abstract":"<p><strong>Purpose: </strong>Deep learning has achieved remarkable progress in low-dose computed tomography (LDCT) denoising; however, radiologists struggle to trust black-box models they cannot verify or control. Zero-shot methods eliminate training data requirements but fail on computed tomography's (CT) spatially correlated noise. We demonstrate that a transparent mathematical operator, when made content-adaptive, can match deep learning performance while remaining fully interpretable.</p><p><strong>Approach: </strong>We introduce Filter2Noise (F2N), which replaces conventional deep networks with an attention-guided bilateral filter that adapts to local anatomy. A lightweight attention module (3.6k parameters) predicts optimal filtering strategies for each image region by analyzing tissue type, texture, and noise characteristics. To enable robust learning from a single noisy image with correlated noise, we develop Euclidean local shuffle, which strategically disrupts noise correlations while preserving anatomical structure, and a multi-scale self-supervised loss that enforces consistency across resolutions.</p><p><strong>Results: </strong>On the Mayo Clinic LDCT Grand Challenge, F2N achieves 39.76 dB peak signal-to-noise ratio, outperforming the next-best zero-shot method by 1.88 dB, while using 360× fewer parameters (3.6k versus 1.3M). Clinical validation on photon-counting CT demonstrates that F2N elevates low-dose images to full-dose quality (no statistically significant difference in contrast-to-noise ratio, <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.10</mn></mrow> </math> ). The learned filtering strategy is fully visualizable: parameter maps reveal content-aware behavior. Radiologists can interactively adjust these parameters post-training to refine denoising in diagnostically critical regions.</p><p><strong>Conclusions: </strong>F2N reconciles competitive performance with complete interpretability and user control, providing radiologists with a verifiable tool that works across scanners and protocols without retraining.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"024004"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesizing breast cancer ultrasound images from healthy samples using latent diffusion models.","authors":"Yannuo Wen, Kathleen M Curran, Xinzhu Wang, Nuala A Healy, John J Healy","doi":"10.1117/1.JMI.13.2.024002","DOIUrl":"https://doi.org/10.1117/1.JMI.13.2.024002","url":null,"abstract":"<p><strong>Purpose: </strong>Breast ultrasound is widely used for cancer screening, but data scarcity and annotation challenges hinder deep learning adoption. Synthetic image generation offers a promising solution to enhance training datasets while preserving patient privacy. However, problems such as inadequate quality of synthesized images and the need for large amounts of data to train the synthesis models remain significant.</p><p><strong>Approach: </strong>We propose a three-stage latent diffusion model (LDM) workflow-enhanced by Vision Transformers and fine-tuned with low-rank adaptation-that synthesizes realistic malignant and benign breast ultrasound images directly from healthy samples while simultaneously generating accurate segmentation masks. Stage division significantly reduces the task complexity of a single synthesis model. Applied to the BUSI dataset (133 healthy, 487 benign, and 210 malignant images), the method generates synthetic cases of each tumor type.</p><p><strong>Results: </strong>A ResNet101 classifier could not reliably distinguish synthetic from real images (AUC = 0.563), indicating high visual plausibility. Quantitative metrics confirmed strong fidelity: Fréchet inception distance = 15.2 and inception score = 1.79, indicating low distributional divergence in feature space and high similarity to real data. When used for training a U-Net segmentation model, the augmented dataset improved the <math><mrow><mi>F</mi> <mn>1</mn></mrow> </math> -score from 0.870 to 0.896, demonstrating substantial gains in diagnostic accuracy.</p><p><strong>Conclusions: </strong>These results show that the proposed three-stage LDM can generate high-quality, anatomically coherent breast cancer images from healthy controls, effectively alleviating data scarcity and enabling more robust training of medical AI systems without compromising clinical realism.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"024002"},"PeriodicalIF":1.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12999972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction.","authors":"Daniel Sleiman, Navchetan Awasthi","doi":"10.1117/1.JMI.13.2.024502","DOIUrl":"10.1117/1.JMI.13.2.024502","url":null,"abstract":"<p><strong>Purpose: </strong>Magnetic resonance imaging (MRI), particularly dynamic contrast-enhanced MRI (DCE-MRI), plays a vital role in breast cancer assessment by highlighting tumor regions. Accurate 3D segmentation of tumors can significantly aid in diagnosis, disease monitoring, and treatment planning. Current state-of-the-art models such as nnU-Net are computationally expensive, with high parameter counts and memory requirements. In this work, we propose a parameter-efficient convolutional neural network-based architecture tailored for breast tumor segmentation in DCE-MRI.</p><p><strong>Approach: </strong>The model integrates lightweight residual blocks into the SegResNet backbone and is trained on the first 3 DCE-MRI phases. We test the addition of the FRLoss from the HCMA-UNet model in the FRLoss ablation. Its encoder-decoder design also enables exploration of the treatment response prediction to neoadjuvant chemotherapy measured by the pathological complete response, using the output of the last encoder block. The last encoder block output is average-pooled and used as input to an XGBoost model with two estimators (max depth 5, learning rate 1).</p><p><strong>Results: </strong>Evaluated on the public MAMA-MIA dataset, our proposed model achieves comparable performance to nnU-Net and SegResNet, with a 0.99% higher Dice score than nnU-Net, while reducing parameter count by 91.5%, FLOPs by 85.05%, and memory usage by 31.94% compared with nnU-Net. Therefore, the proposed model is significantly more efficient than nnU-Net and also offers superior accuracy to the Mamba-based baseline, even though the Mamba baseline remains computationally lighter demonstrated by its faster inference speed and lower giga floating point operations per second (38 versus 87). The XGBoost model for treatment response prediction does not demonstrate competitive performance with a balanced accuracy of 57.2% and receiver operating characteristic-area under curve of 0.542.</p><p><strong>Conclusion: </strong>Our results demonstrate that parameter-efficient models can achieve competitive performance in DCE-MRI tumor segmentation.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"024502"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic dental crown generation with spatial constraint modeling.","authors":"Golriz Hosseinimanesh, Farida Cheriet, Ammar Alsheghri, Victoria-Mae Carrière, Julia Keren, Francois Guibault","doi":"10.1117/1.JMI.13.2.023501","DOIUrl":"10.1117/1.JMI.13.2.023501","url":null,"abstract":"<p><strong>Purpose: </strong>Deep learning algorithms offer the potential to automate dental crown generation, reducing time-intensive manual design in dental laboratories. However, achieving crowns suitable for direct clinical use requires both geometric precision and functional accuracy to minimize post-generation adjustments. Current approaches focus primarily on shape completion without explicitly modeling critical spatial relationships, including margin line boundaries, occlusal contact patterns, and adjacent tooth interactions. These limitations result in generated crowns lacking spatial accuracy necessary for direct clinical application.</p><p><strong>Approach: </strong>We present a comprehensive framework employing transformer encoder-decoder architecture integrated with differentiable Poisson surface reconstruction for direct dental crown mesh generation. The framework incorporates two key innovations to address clinical limitations. First, margin line data is integrated as direct network input, concatenated with master and antagonist arch geometries, providing explicit boundary constraints during crown generation. Second, spatial constraint losses ensure anatomically valid relationships through antagonist interaction loss for proper occlusal contact patterns and intersection loss to prevent crown penetration into adjacent teeth.</p><p><strong>Results: </strong>The proposed framework achieves substantial improvements over existing state-of-the-art methods, with geometric accuracy gains ranging from 35.9% to 40.6% across evaluation metrics. Margin line integration yields a 31.2% improvement in geometric precision, with maximum boundary errors reduced from 1.37 to 0.74 mm and a 58.4% reduction in variability. Antagonist interaction loss provides 9.51% improvement in occlusal alignment, while intersection loss substantially reduces crown penetration into adjacent teeth.</p><p><strong>Conclusions: </strong>Substantial performance improvements validate the effectiveness of integrating spatial constraint modeling and direct margin line input into the generation process, establishing a foundation for clinical deployment of automated dental crown design systems.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"023501"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147785802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of controlled attenuation parameter-based liver steatosis via raw ultrasound data from handheld devices.","authors":"Jakob Schäfer, Charlotte Herzog, Tina Gabriel, Julian Kober, Toennis Trittler, Edgar Dorausch, Omid Chaghaneh, Thomas Karlas, Cornelius Kühnöl, Antje Naas, Gerhard Fettweis, Franz Brinkmann, Nicole Kampfrath, Jochen Hampe, Carolin Schneider, Moritz Herzog","doi":"10.1117/1.JMI.13.2.027001","DOIUrl":"https://doi.org/10.1117/1.JMI.13.2.027001","url":null,"abstract":"<p><strong>Purpose: </strong>Assessment of liver steatosis is primarily performed through visual evaluation during ultrasound examinations. A more objective approach relies on quantifying ultrasound attenuation, typically using devices such as the FibroScan® or elastography integrated into high-end ultrasound systems-which offer limited accessibility. By contrast, handheld ultrasound devices (HHUDs) are more affordable and widely available. Using raw ultrasound data to get deeper insights into liver tissue characteristics could turn HHUDs into valuable diagnostic tools. We hypothesized that the frequency-specific attenuation of raw ultrasound data acquired with handheld devices correlates with the controlled attenuation parameter (CAP) obtained through vibration-controlled transient elastography via FibroScan.</p><p><strong>Approach: </strong>In an exploratory, single-center study, raw data from 395 participants scheduled for CAP measurement were collected using HHUDs. Of these, 304 participants were included in the final analysis; 91 were excluded due to incomplete data. Using the raw data from the HHUDs, a method based on short-time fast Fourier transform was applied to calculate the frequency-specific attenuation. The results were then correlated with the CAP values.</p><p><strong>Results: </strong>Overall, the attenuation of the radiofrequency data showed a strong linear correlation with CAP values ( <math><mrow><mi>r</mi> <mo>=</mo> <mn>0.672</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), although the strength of correlation varied significantly across frequencies (r_min = 0.443 at 0.75 MHz, r_max = 0.721 at 3.75 MHz), with the highest correlation, equaling results from studies with high-end ultrasound devices.</p><p><strong>Conclusion: </strong>HHUDs capable of acquiring raw data may serve as objective and accessible screening tools for liver steatosis, potentially improving treatment monitoring.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"027001"},"PeriodicalIF":1.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12997074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147487893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.","authors":"Krisha Anant, Juanita Hernández López, Junjie Cui, Sneha Das Gupta, Debbie L Bennett, Aimilia Gastounioti","doi":"10.1117/1.JMI.13.2.024503","DOIUrl":"10.1117/1.JMI.13.2.024503","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to evaluate the performance of different deep learning (DL) architectures in breast density classification using digital mammograms (DMs) and synthetic mammograms (SMs) from digital breast tomosynthesis (DBT).</p><p><strong>Approach: </strong>We retrospectively analyzed routine mammographic screening exams (Selenia Dimensions, Hologic Inc.) acquired between 2015 and 2018 at our institution. Each mammogram dataset (DM and SM) included 10,000 exams representing all four breast imaging reporting and data system density categories (a to d). We used ResNet-50, EfficientNet-B0, and DenseNet-121 architectures, separately fine-tuned for breast density classification with DM and SM. Classification accuracy was assessed on 10% unseen test sets in four-category (a to d) and binary (nondense versus dense) scenarios. Evaluations also considered mammogram view (craniocaudal [CC] versus mediolateral-oblique [MLO] view) and race (White versus Black women).</p><p><strong>Results: </strong>DL architectures showed detectable, yet small, differences in classification accuracy regardless of mammogram format. ResNet-50 achieved a four-category accuracy of 0.727 (95% CI: [0.713, 0.740]) for DM, higher than 0.713 (95% CI: [0.699, 0.728]) for SM ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.151</mn></mrow> </math> ). EfficientNet-B0 and DenseNet-121 showed similar trends. DM-SM differences for binary classification were of similar magnitude but statistically significant ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ), with test accuracies ranging from 0.871 to 0.920. The MLO view generally outperformed the CC view, and the results were consistent across racial groups.</p><p><strong>Conclusions: </strong>We highlight that various DL architectures perform effectively in breast density classification, emphasizing the significance of mammogram format and view, though results may vary with different vendors. These insights are crucial for enhancing DL-based breast density assessment, especially during the shift from DM to DBT.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"13 2","pages":"024503"},"PeriodicalIF":2.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147785069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}