Journal of Medical Imaging最新文献

{"title":"Self-supervised learning for interventional image analytics: toward robust device trackers.","authors":"Saahil Islam, Venkatesh N Murthy, Dominik Neumann, Badhan Kumar Das, Puneet Sharma, Andreas Maier, Dorin Comaniciu, Florin C Ghesu","doi":"10.1117/1.JMI.11.3.035001","DOIUrl":"https://doi.org/10.1117/1.JMI.11.3.035001","url":null,"abstract":"<p><strong>Purpose: </strong>The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion.</p><p><strong>Approach: </strong>To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model.</p><p><strong>Results: </strong>Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a <math><mrow><mn>3</mn><mo>×</mo></mrow></math> faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance.</p><p><strong>Conclusions: </strong>The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11094643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959594","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":"Surgical planning in virtual reality: a systematic review","authors":"Moritz Queisner, Karl Eisenträger","doi":"10.1117/1.jmi.11.6.062603","DOIUrl":"https://doi.org/10.1117/1.jmi.11.6.062603","url":null,"abstract":"","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems","authors":"Kaiyan Li, Umberto Villa, Hua Li, M. Anastasio","doi":"10.1117/1.jmi.11.2.026002","DOIUrl":"https://doi.org/10.1117/1.jmi.11.2.026002","url":null,"abstract":"","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization.","authors":"Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A Landman","doi":"10.1117/1.JMI.11.2.024008","DOIUrl":"https://doi.org/10.1117/1.JMI.11.2.024008","url":null,"abstract":"<p><strong>Purpose: </strong>Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured.</p><p><strong>Approach: </strong>To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space.</p><p><strong>Results: </strong>Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge Beyond the Cranial Vault (BTCV) dataset demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore longitudinal study of aging dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.</p><p><strong>Conclusion: </strong>This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140870458","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":"Welcome to the second issue of the <i>Journal of Medical Imaging</i> (JMI) for the 2024 year!","authors":"","doi":"10.1117/1.JMI.11.2.020101","DOIUrl":"https://doi.org/10.1117/1.JMI.11.2.020101","url":null,"abstract":"<p><p>Editor-in-Chief Bennett A. Landman (Vanderbilt University) provides opening remarks for the current issue of JMI, with specific commentary on medical imaging community \"challenges\" and their potential to coalesce creative energies.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11057461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873053","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":"Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.","authors":"Julia Kar, Michael V Cohen, Samuel A McQuiston, Teja Poorsala, Christopher M Malozzi","doi":"10.1117/1.JMI.11.2.024003","DOIUrl":"10.1117/1.JMI.11.2.024003","url":null,"abstract":"<p><p><b>Purpose:</b> The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). <b>Approach:</b> A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach's alpha (<math><mrow><mi>C</mi><mtext>-</mtext><mi>α</mi></mrow></math>) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations. <b>Results:</b> Myocardial classification results against ground-truth were <math><mrow><mtext>Dice</mtext><mo>=</mo><mn>0.89</mn></mrow></math>, <math><mrow><mi>APD</mi><mo>=</mo><mn>2.4</mn><mtext>  </mtext><mi>mm</mi></mrow></math>, and <math><mrow><mtext>accuracy</mtext><mo>=</mo><mn>97</mn><mo>%</mo></mrow></math> for the validation set and <math><mrow><mtext>Dice</mtext><mo>=</mo><mn>0.90</mn></mrow></math>, <math><mrow><mi>APD</mi><mo>=</mo><mn>2.5</mn><mtext>  </mtext><mi>mm</mi></mrow></math>, and <math><mrow><mtext>accuracy</mtext><mo>=</mo><mn>97</mn><mo>%</mo></mrow></math> for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were <math><mrow><mi>CI</mi><mo>=</mo><mo>-</mo><mn>1.36</mn><mo>%</mo></mrow></math> to 2.42%, p-value < 0.001 for LVEF (<math><mrow><mn>58</mn><mo>±</mo><mn>5</mn><mo>%</mo></mrow></math> versus <math><mrow><mn>57</mn><mo>±</mo><mn>6</mn><mo>%</mo></mrow></math>), and <math><mrow><mi>CI</mi><mo>=</mo><mo>-</mo><mn>0.71</mn><mo>%</mo></mrow></math> to 0.63%, p-value < 0.001 for longitudinal strain (<math><mrow><mo>-</mo><mn>15</mn><mo>±</mo><mn>2</mn><mo>%</mo></mrow></math> versus <math><mrow><mo>-</mo><mn>15</mn><mo>±</mo><mn>3</mn><mo>%</mo></mrow></math>). <b>Conclusions:</b> The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177113","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":"Prognostic value of different discretization parameters in ¹⁸fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.","authors":"Breylon A Riley, Jack B Stevens, Xiang Li, Zhenyu Yang, Chunhao Wang, Yvonne M Mowery, David M Brizel, Fang-Fang Yin, Kyle J Lafata","doi":"10.1117/1.JMI.11.2.024007","DOIUrl":"10.1117/1.JMI.11.2.024007","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer.</p><p><strong>Approach: </strong>A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (<math><mrow><mi>N</mi><mo>=</mo><mn>69</mn></mrow></math>; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment ¹⁸fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model.</p><p><strong>Results: </strong>FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (<math><mrow><mi>p</mi><mo>=</mo><mn>0.0158</mn></mrow></math> and <math><mrow><mi>p</mi><mo>=</mo><mn>0.0180</mn></mrow></math>, respectively; log-rank test).</p><p><strong>Conclusions: </strong>Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10966359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319488","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":"Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.","authors":"Anton Orlichenko, Kuan-Jui Su, Hui Shen, Hong-Wen Deng, Yu-Ping Wang","doi":"10.1117/1.JMI.11.2.024010","DOIUrl":"https://doi.org/10.1117/1.JMI.11.2.024010","url":null,"abstract":"<p><strong>Purpose: </strong>Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort.</p><p><strong>Approach: </strong>We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected <math><mrow><mi>t</mi></mrow></math>-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets.</p><p><strong>Results: </strong>We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected <math><mrow><mi>p</mi><mo><</mo><msup><mn>10</mn><mrow><mo>-</mo><mn>15</mn></mrow></msup></mrow></math>) that occurs in male, female, older subject (<math><mrow><mo>></mo><mn>65</mn></mrow></math> years old), and younger subject (<math><mrow><mo><</mo><mn>55</mn></mrow></math> years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.</p><p><strong>Conclusions: </strong>We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11009525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859754","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":"Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.","authors":"Mojtaba Zarei, Ehsan Abadi, Liesbeth Vancoillie, Ehsan Samei","doi":"10.1117/1.JMI.11.2.025501","DOIUrl":"https://doi.org/10.1117/1.JMI.11.2.025501","url":null,"abstract":"<p><strong>Background: </strong>The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use.</p><p><strong>Purpose: </strong>The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs.</p><p><strong>Method: </strong>MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA).</p><p><strong>Results: </strong>The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions.</p><p><strong>Conclusion: </strong>The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11047768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874903","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":"Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification.","authors":"Michael H Udin, Sara Armstrong, Alice Kai, Scott Doyle, Ciprian N Ionita, Saraswati Pokharel, Umesh C Sharma","doi":"10.1117/1.JMI.11.2.024503","DOIUrl":"10.1117/1.JMI.11.2.024503","url":null,"abstract":"<p><strong>Purpose: </strong>Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification.</p><p><strong>Approach: </strong>CMR images from 279 patients (151 IMS, 128 control) were classified for IMS presence using two convolutional neural networks (CNNs) and TM, both with and without LWP. Evaluation metrics included accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUROC), and processing time. External testing dataset analysis encompassed patient-level classifications (PLCs) and a CNN versus TM classification comparison (CVTCC).</p><p><strong>Results: </strong>LWP enhanced the speed of both CNNs (4.9x) and TM (21.9x). Furthermore, in the absence of LWP, TM outpaced CNNs by over 10x, while with LWP, TM was more than 100x faster. Additionally, TM performed similarly to the CNNs in accuracy, sensitivity, specificity, F1-score, and AUROC, with PLCs demonstrating improvements across all five metrics. Moreover, the CVTCC revealed a substantial 90.9% agreement.</p><p><strong>Conclusions: </strong>Our results highlight the effectiveness of LWP and TM in streamlining IMS classification. Anticipated enhancements to LWP's region of interest (ROI) isolation and TM's ROI targeting are expected to boost accuracy, positioning them as a potential alternative to CNNs for IMS classification, supporting the need for further research.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10956816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140207952","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}