Journal of Medical Imaging最新文献

{"title":"DCEF-AVNet: multi-scale feature fusion and attention mechanism-guided brain tumor segmentation network.","authors":"Linlin Wang, Tong Zhang, Chuanyun Wang, Qian Gao, Zhongyi Li, Jing Shao","doi":"10.1117/1.JMI.12.2.024503","DOIUrl":"10.1117/1.JMI.12.2.024503","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate and efficient automatic segmentation of brain tumors is critical for diagnosis and treatment. However, the diversity in the appearance, location, and shape of brain tumors and their subregions, coupled with complex boundaries, presents significant challenges. We aim to improve segmentation accuracy by addressing limitations in V-Net, including insufficient utilization of multi-scale features and difficulties in managing complex spatial relationships and long-range dependencies.</p><p><strong>Approach: </strong>We propose an improved network structure, dynamic convolution enhanced fusion axial V-Net (DCEF-AVNet), which integrates an enhanced feature fusion module and axial attention mechanisms. The feature fusion module integrates dynamic convolution with a redesigned skip connection strategy to effectively combine multi-scale features, reducing feature inconsistencies and improving representation capability. Axial attention mechanisms are introduced at encoder-decoder connections to manage spatial relationships and alleviate long-range dependency issues. The network was evaluated using the BraTS2021 dataset, with performance measured in terms of Dice coefficients and Hausdorff distances.</p><p><strong>Results: </strong>DCEF-AVNet achieved Dice coefficients of 92.49%, 91.35%, and 91.96% for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions, respectively, significantly outperforming baseline methods. The model also demonstrated robust performance across multiple runs, with consistently low standard deviations in metrics.</p><p><strong>Conclusions: </strong>The integration of dynamic convolution, enhanced feature fusion, and axial attention mechanisms enables DCEF-AVNet to deliver superior segmentation accuracy and robustness. These results underscore its potential for advancing automated brain tumor segmentation and improving clinical decision-making.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 2","pages":"024503"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694060","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":"Ensemble of fine-tuned machine learning models for hysterectomy prediction in pregnant women using magnetic resonance images.","authors":"Vishnu Vardhan Reddy Kanamata Reddy, Michael Villordon, Quyen N Do, Yin Xi, Matthew A Lewis, Christina L Herrera, David Owen, Catherine Y Spong, Diane M Twickler, Baowei Fei","doi":"10.1117/1.JMI.12.2.024502","DOIUrl":"10.1117/1.JMI.12.2.024502","url":null,"abstract":"<p><strong>Purpose: </strong>Identifying pregnant patients at high risk of hysterectomy before giving birth informs clinical management and improves outcomes. We aim to develop machine learning models to predict hysterectomy in pregnant women with placenta accreta spectrum (PAS).</p><p><strong>Approach: </strong>We developed five machine learning models using information from magnetic resonance images and combined them with topographic maps and radiomic features to predict hysterectomy. The models were trained, optimized, and evaluated on data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively.</p><p><strong>Results: </strong>We assessed the models individually as well as using an ensemble approach. When these models are combined, the ensembled model produced the best performance and achieved an area under the curve of 0.90, a sensitivity of 90.0%, and a specificity of 90.0% for predicting hysterectomy.</p><p><strong>Conclusions: </strong>Various machine learning models were developed to predict hysterectomy in pregnant women with PAS, which may have potential clinical applications to help improve patient management.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 2","pages":"024502"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11915718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665012","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":"Identifying texture features from structural magnetic resonance imaging scans associated with Tourette's syndrome using machine learning.","authors":"Murilo Costa de Barros, Kauê Tartarotti Nepomuceno Duarte, Chia-Jui Hsu, Wang-Tso Lee, Marco Antonio Garcia de Carvalho","doi":"10.1117/1.JMI.12.2.026001","DOIUrl":"https://doi.org/10.1117/1.JMI.12.2.026001","url":null,"abstract":"<p><strong>Purpose: </strong>Tourette syndrome (TS) is a neurodevelopmental disorder characterized by neurophysiological and neuroanatomical changes, primarily affecting individuals aged 2 to 18. Involuntary motor and vocal tics are common features of this syndrome. Currently, there is no curative therapy for TS, only psychological treatments or medications that temporarily manage the tics. The absence of a definitive diagnostic tool complicates the differentiation of TS from other neurological and psychological conditions.</p><p><strong>Approach: </strong>We aim to enhance the diagnosis of TS through the classification of structural magnetic resonance scans. Our methodology comprises four sequential steps: (1) image acquisition, data were generated for the National Taiwan University, composing images of pediatric magnetic resonance imaging (MRI); (2) pre-processing, involving anatomical structural segmentation using reesurfer software; (3) feature extraction, where texture features in volumetric images are obtained; and (4) image classification, employing support vector machine and naive Bayes classifiers to determine the presence of TS.</p><p><strong>Results: </strong>The analysis indicated significant changes in the regions of the limbic system, such as the thalamus and amygdala, and regions outside the limbic system such as medial orbitofrontal cortex and insula, which are strongly associated with TS.</p><p><strong>Conclusions: </strong>Our findings suggest that texture features derived from sMRI scans can aid in the diagnosis of TS by highlighting critical brain regions involved in the disorder. The proposed method holds promise for improving diagnostic accuracy and understanding the neuroanatomical underpinnings of TS.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 2","pages":"026001"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543091","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":"Evaluation of state-of-the-art deep learning models in the segmentation of the left and right ventricles in parasternal short-axis echocardiograms.","authors":"Julian R Cuellar, Vu Dinh, Manjula Burri, Julie Roelandts, James Wendling, Jon D Klingensmith","doi":"10.1117/1.JMI.12.2.024002","DOIUrl":"10.1117/1.JMI.12.2.024002","url":null,"abstract":"<p><strong>Purpose: </strong>Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. Deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of important metrics with the potential to aid in diagnosing cardio-pulmonary diseases and other cardiomyopathies. Evaluating state-of-the-art models with small datasets can reveal if they improve performance on limited data.</p><p><strong>Approach: </strong>PSAX-echo was performed on 33 volunteer women. An experienced cardiologist identified end-diastole and end-systole frames from 387 scans, and expert observers manually traced the contours of the cardiac structures. Traced frames were pre-processed and used to create labels to train two domain-specific (Unet-Resnet101 and Unet-ResNet50), and four general-domain [three segment anything (SAM) variants, and the Detectron2] deep-learning models. The performance of the models was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and difference in cross-sectional area (DCSA).</p><p><strong>Results: </strong>The Unet-Resnet101 model provided superior performance in the segmentation of the ventricles with 0.83, 4.93 pixels, and <math><mrow><mn>106</mn> <mtext>  </mtext> <msup><mrow><mtext>pixel</mtext></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> on average for DSC, HD, and DCSA, respectively. A fine-tuned MedSAM model provided a performance of 0.82, 6.66 pixels, and <math><mrow><mn>1252</mn> <mtext>  </mtext> <msup><mrow><mtext>pixel</mtext></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> , whereas the Detectron2 model provided 0.78, 2.12 pixels, and <math><mrow><mn>116</mn> <mtext>  </mtext> <msup><mrow><mtext>pixel</mtext></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> for the same metrics, respectively.</p><p><strong>Conclusions: </strong>Deep-learning models are suitable for the segmentation of the left and right ventricles in PSAX-echo. We demonstrated that domain-specific trained models such as Unet-ResNet provide higher accuracy for echo segmentation than general-domain segmentation models when working with small and locally acquired datasets.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 2","pages":"024002"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11943840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732236","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":"Two-phase radial endobronchial ultrasound bronchoscopy registration.","authors":"Wennan Zhao, Trevor Kuhlengel, Qi Chang, Vahid Daneshpajooh, Yuxuan He, Austin Kao, Rebecca Bascom, Danish Ahmad, Yu Maw Htwe, Jennifer Toth, Thomas Schaer, Leslie Brewer, Rachel Hilliard, William E Higgins","doi":"10.1117/1.JMI.12.2.025001","DOIUrl":"10.1117/1.JMI.12.2.025001","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Lung cancer remains the leading cause of cancer death. This has brought about a critical need for managing peripheral regions of interest (ROIs) in the lungs, be it for cancer diagnosis, staging, or treatment. The state-of-the-art approach for assessing peripheral ROIs involves bronchoscopy. To perform the procedure, the physician first navigates the bronchoscope to a preplanned airway, aided by an assisted bronchoscopy system. They then confirm an ROI's specific location and perform the requisite clinical task. Many ROIs, however, are extraluminal and invisible to the bronchoscope's field of view. For such ROIs, current practice dictates using a supplemental imaging method, such as fluoroscopy, cone-beam computed tomography (CT), or radial endobronchial ultrasound (R-EBUS), to gather additional ROI location information. Unfortunately, fluoroscopy and cone-beam CT require substantial radiation and lengthen procedure time. As an alternative, R-EBUS is a safer real-time option involving no radiation. Regrettably, existing assisted bronchoscopy systems offer no guidance for R-EBUS confirmation, forcing the physician to resort to an unguided guess-and-check approach for R-EBUS probe placement-an approach that can produce R-EBUS placement errors exceeding 30 deg, an error that can result in missing many ROIs. Thus, because of physician skill variations, biopsy success rates using R-EBUS for ROI confirmation have varied greatly from 31% to 80%. This situation obliges the physician to turn to a radiation-based modality to gather sufficient information for ROI confirmation. We propose a two-phase registration method that provides guidance for R-EBUS probe placement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Approach: &lt;/strong&gt;After the physician navigates the bronchoscope to the airway near a target ROI, the two-phase registration method begins by registering a virtual bronchoscope to the real bronchoscope. A virtual 3D R-EBUS probe model is then registered to the real R-EBUS probe shape depicted in the bronchoscopic video using an iterative region-based alignment method drawing on a level-set-based optimization. This synchronizes the guidance system to the target ROI site. The physician can now perform the R-EBUS scan to confirm the ROI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We validated the method's efficacy for localizing extraluminal ROIs with a series of three studies. First, for a controlled phantom study, we observed that the mean accumulated position and direction errors (accounting for both registration phases) were 1.94 mm and 3.74 deg (equivalent to 1.30 mm position error for a 20 mm biopsy needle), respectively. Next, for a live animal study, these errors were 2.81 mm and 4.79 deg (2.41 mm biopsy needle error), respectively. For 100% of the ROIs considered in these two studies, the method enabled visualization of an ROI via R-EBUS in under 3 min per ROI. Finally, initial operating-room tests on lung cancer patients indicated the method's efficacy,","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 2","pages":"025001"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587704","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":"Evaluation of the flying focal spot technology in a wide-angle digital breast tomosynthesis system.","authors":"Katrien Houbrechts, Nicholas Marshall, Lesley Cockmartin, Hilde Bosmans","doi":"10.1117/1.JMI.12.S1.S13009","DOIUrl":"10.1117/1.JMI.12.S1.S13009","url":null,"abstract":"<p><strong>Purpose: </strong>We characterize the flying focal spot (FFS) technology in digital breast tomosynthesis (DBT), designed to overcome source motion blurring.</p><p><strong>Approach: </strong>A wide-angle DBT system with continuous gantry and focus motion (\"uncompensated focus\") and a system with FFS were compared for image sharpness and lesion detectability. The modulation transfer function (MTF) was assessed as a function of height in the projections and reconstructed images, along with lesion detectability using the contrast detail phantom for mammography (CDMAM) and the L1 phantom.</p><p><strong>Results: </strong>For the uncompensated focus system, the spatial frequency for 25% MTF value ( <math> <mrow><msub><mi>f</mi> <mrow><mn>25</mn> <mo>%</mo></mrow> </msub> </mrow> </math> ) measured at 2, 4, and 6 cm in DBT projections fell by 35%, 49%, and 59%, respectively in the tube-travel direction compared with the FFS system. There was no significant difference in <math> <mrow><msub><mi>f</mi> <mrow><mn>25</mn> <mo>%</mo></mrow> </msub> </mrow> </math> for the front-back and tube-travel directions for the FFS unit. The in-plane MTF in the tube-travel direction also improved with the FFS technology.The threshold gold thickness ( <math> <mrow><msub><mi>T</mi> <mi>t</mi></msub> </mrow> </math> ) for the 0.16-mm diameter discs of contrast detail phantom for mammography (CDMAM) improved for the FFS system in DBT mode, especially at greater heights above the table; <math> <mrow><msub><mi>T</mi> <mi>t</mi></msub> </mrow> </math> at 45 and 65 mm improved by 16% and 24%, respectively, compared with the uncompensated focus system. In addition, improvements in calcification and mass detection in a structured background were observed for DBT and synthetic mammography. The FFS system demonstrated faster scan times (4.8 s versus 21.7 s), potentially reducing patient motion artifacts.</p><p><strong>Conclusions: </strong>The FFS technology offers isotropic resolution, improved small detail detectability, and faster scan times in DBT mode compared with the traditional continuous gantry and focus motion approach.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13009"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786586","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":"Pericoronary adipose tissue feature analysis in computed tomography calcium score images in comparison to coronary computed tomography angiography.","authors":"Yingnan Song, Hao Wu, Juhwan Lee, Justin Kim, Ammar Hoori, Tao Hu, Vladislav Zimin, Mohamed Makhlouf, Sadeer Al-Kindi, Sanjay Rajagopalan, Chun-Ho Yun, Chung-Lieh Hung, David L Wilson","doi":"10.1117/1.JMI.12.1.014503","DOIUrl":"10.1117/1.JMI.12.1.014503","url":null,"abstract":"<p><strong>Purpose: </strong>We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.</p><p><strong>Approach: </strong>To identify coronaries in CTCS images that have subtle visual evidence of vessels, we registered CTCS with paired CCTA images having coronary labels. We developed an \"axial-disk\" method giving regions for analyzing PCAT features in three main coronary arteries. We analyzed hand-crafted and radiomic features using univariate and multivariate logistic regression prediction of MACE and compared results against those from CCTA.</p><p><strong>Results: </strong>Registration accuracy was sufficient to enable the identification of PCAT regions in CTCS images. Motion or beam hardening artifacts were often prevalent in \"high-contrast\" CCTA but not CTCS. Mean HU and volume were increased in both CTCS and CCTA for the MACE group. There were significant positive correlations between some CTCS and CCTA features, suggesting that similar characteristics were obtained. Using hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.83/0.79 and 0.83/0.77, respectively, whereas Agatston gave AUC = 0.73.</p><p><strong>Conclusions: </strong>Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014503"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048280","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":"2024 List of Reviewers.","authors":"","doi":"10.1117/1.JMI.12.1.010102","DOIUrl":"https://doi.org/10.1117/1.JMI.12.1.010102","url":null,"abstract":"<p><p>Thanks to reviewers who served the Journal of Medical Imaging in 2024.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"010102"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11753298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029986","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":"Deep learning CT image restoration using system blur and noise models.","authors":"Yijie Yuan, Grace J Gang, J Webster Stayman","doi":"10.1117/1.JMI.12.1.014003","DOIUrl":"10.1117/1.JMI.12.1.014003","url":null,"abstract":"<p><strong>Purpose: </strong>The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities such as computed tomography. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. We present a method that leverages both degraded image inputs and a characterization of the system's blur and noise to combine modeling and deep learning approaches.</p><p><strong>Approach: </strong>Different methods to integrate these auxiliary inputs are presented, namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any convolutional neural network architecture.</p><p><strong>Results: </strong>The proposed model shows superior performance compared with baseline models lacking auxiliary inputs. Evaluations are based on the average peak signal-to-noise ratio and structural similarity index measure, selected examples of top and bottom 10% performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. In addition, the proposed model exhibits a degree of robustness when the blur and noise parameters deviate from their true values.</p><p><strong>Conclusion: </strong>Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014003"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11788843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190925","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":"Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.","authors":"Anders Tingberg, Victor Dahlblom, Magnus Dustler, Daniel Förnvik, Kristin Johnson, Pontus Timberg, Sophia Zackrisson","doi":"10.1117/1.JMI.12.S1.S13006","DOIUrl":"10.1117/1.JMI.12.S1.S13006","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future.</p><p><strong>Approach: </strong>In two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond.</p><p><strong>Results: </strong>Our studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one-the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem.</p><p><strong>Conclusions: </strong>We have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13006"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11501043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510422","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}