Journal of Medical Imaging最新文献

{"title":"Cone-beam CT with a noncircular (sine-on-sphere) orbit: imaging performance of a clinical system for image-guided interventions.","authors":"A Kyle Jones, Moiz Ahmad, Shaan M Raza, Stephen R Chen, Jeffrey H Siewerdsen","doi":"10.1117/1.JMI.11.4.043503","DOIUrl":"10.1117/1.JMI.11.4.043503","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to compare the imaging performance of a cone-beam CT (CBCT) imaging system with noncircular scan protocols (sine-on-sphere) to a conventional circular orbit.</p><p><strong>Approach: </strong>A biplane C-arm system (ARTIS Icono; Siemens Healthineers) capable of circular and noncircular CBCT acquisition was used, with the latter orbit (sine-on-sphere, \"Sine Spin\") executing a sinusoidal motion with <math><mrow><mo>±</mo> <mn>10</mn> <mtext>  </mtext> <mi>deg</mi></mrow> </math> tilt amplitude over the half-scan orbit. A test phantom was used for the characterization of image uniformity, noise, noise-power spectrum (NPS), spatial resolution [modulation transfer function (MTF) in axial and oblique directions], and cone-beam artifacts. Findings were interpreted using an anthropomorphic head phantom with respect to pertinent tasks in skull base neurosurgery.</p><p><strong>Results: </strong>The noncircular scan protocol exhibited several advantages associated with improved 3D sampling-evident in the NPS as filling of the null cone about the <math> <mrow><msub><mi>f</mi> <mi>z</mi></msub> </mrow> </math> spatial frequency axis and reduction of cone-beam artifacts. The region of support at the longitudinal extrema was reduced from 16 to <math><mrow><mo>∼</mo> <mn>12</mn> <mtext>  </mtext> <mi>cm</mi></mrow> </math> at a radial distance of 6.5 cm. Circular and noncircular orbits exhibited nearly identical image uniformity and quantum noise, demonstrating cupping of <math><mrow><mo>-</mo> <mn>16.7</mn> <mo>%</mo></mrow> </math> and overall noise of <math><mrow><mo>∼</mo> <mn>27</mn> <mtext>  </mtext> <mi>HU</mi></mrow> </math> . Although both the radially averaged axial MTF ( <math> <mrow><msub><mi>f</mi> <mrow><mi>x</mi> <mo>,</mo> <mi>y</mi></mrow> </msub> </mrow> </math> ) and 45 deg oblique MTF ( <math> <mrow><msub><mi>f</mi> <mrow><mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi></mrow> </msub> </mrow> </math> ) were <math><mrow><mo>∼</mo> <mn>20</mn> <mo>%</mo></mrow> </math> lower for the noncircular orbit compared with the circular orbit at the default full reconstruction field of view (FOV), there was no difference in spatial resolution for the medium reconstruction FOV (smaller voxel size). Differences in the perceptual image quality for the anthropomorphic phantom reinforced the objective, quantitative findings, including reduced beam-hardening and cone-beam artifacts about structures of interest in the skull base.</p><p><strong>Conclusions: </strong>Image quality differences between circular and noncircular CBCT orbits were quantitatively evaluated on a clinical system in the context of neurosurgery. The primary performance advantage for the noncircular orbit was the improved sampling and elimination of cone-beam artifacts.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056921","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 human observer impression of X-ray fluoroscopy and angiography image quality with technical changes to image quality.","authors":"Jelena M Mihailovic, Yoshihisa Kanaji, Daniel Miller, Malcolm R Bell, Kenneth A Fetterly","doi":"10.1117/1.JMI.11.4.045502","DOIUrl":"10.1117/1.JMI.11.4.045502","url":null,"abstract":"<p><strong>Purpose: </strong>Spatio-temporal variability in clinical fluoroscopy and cine angiography images combined with nonlinear image processing prevents the application of traditional image quality measurements in the cardiac catheterization laboratory. We aimed to develop and validate methods to measure human observer impressions of the image quality.</p><p><strong>Approach: </strong>Multi-frame images of the thorax of a euthanized pig were acquired to provide an anatomical background. The detector dose was varied from 6 to 200 nGy (increments 2×), and 0.6 and 1.0 mm focal spots were used. Two coronary stents with/without 0.5 mm separation and a synthetic right coronary artery (RCA) with hemispherical defects were embedded into the background images as test objects. The quantitative observer ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>17</mn></mrow> </math> ) performance was measured using a two-alternating forced-choice test of whether stents were separated and by a count of visible right coronary artery defects. Qualitative impressions of noise, spatial resolution, and overall image quality were measured using a visual analog scale (VAS). A paired <math><mrow><mi>t</mi></mrow> </math> -test and multinomial logistic regression model were used to identify statistically significant factors affecting the observer's impression image quality.</p><p><strong>Results: </strong>The proportion of correct detection of stent separation and the number of reported right coronary artery defects changed significantly with detector dose increment in the 6 to 100 nGy ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). Although a trend favored the 0.6 versus 1.0 mm focal spot for these quantitative assessments, this was insignificant. Visual analog scale measurements changed significantly with detector dose increments in the range of 24 to 100 nGy and focal spot size ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). The application of multinomial logistic regression analysis to observer VAS scores demonstrated sensitivity matching of the paired <math><mrow><mi>t</mi></mrow> </math> -test applied to quantitative observer performance measurements.</p><p><strong>Conclusions: </strong>Both quantitative and qualitative measurements of observer impression of the image quality were sensitive to image quality changes associated with changing the detector dose and focal spot size. These findings encourage future work that uses qualitative image quality measurements to assess clinical fluoroscopy and angiography image quality.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917779","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":"Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.","authors":"Mohammad Mehdi Farhangi, Michael Maynord, Cornelia Fermüller, Yiannis Aloimonos, Berkman Sahiner, Nicholas Petrick","doi":"10.1117/1.JMI.11.4.044507","DOIUrl":"10.1117/1.JMI.11.4.044507","url":null,"abstract":"<p><strong>Purpose: </strong>Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system.</p><p><strong>Approach: </strong>Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline.</p><p><strong>Results: </strong>We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom.</p><p><strong>Conclusions: </strong>The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907942","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":"Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation.","authors":"Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, Chunliang Wang","doi":"10.1117/1.JMI.11.4.044001","DOIUrl":"10.1117/1.JMI.11.4.044001","url":null,"abstract":"<p><strong>Purpose: </strong>Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.</p><p><strong>Approach: </strong>We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.</p><p><strong>Results: </strong>Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.</p><p><strong>Conclusions: </strong>Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11231955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581239","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":"Field-of-view extension for brain diffusion MRI via deep generative models.","authors":"Chenyu Gao, Shunxing Bao, Michael E Kim, Nancy R Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter A Kukull, Arthur W Toga, Derek B Archer, Timothy J Hohman, Bennett A Landman, Zhiyuan Li","doi":"10.1117/1.JMI.11.4.044008","DOIUrl":"10.1117/1.JMI.11.4.044008","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Approach: &lt;/strong&gt;We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;PSNR&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;22.397&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;SSIM&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.905&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , &lt;math&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PSNR&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;1300&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;22.479&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , and &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;SSIM&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;1300&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.893&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;PSNR&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;21.304&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;SSIM&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.892&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;PSNR&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;1300&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;21.599&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; , and &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;SSIM&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mn&gt;1300&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.877&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.001&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; ) on both the WRAP and NACC datasets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associa","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056922","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":"AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.","authors":"Heather M Whitney, Roni Yoeli-Bik, Jacques S Abramowicz, Li Lan, Hui Li, Ryan E Longman, Ernst Lengyel, Maryellen L Giger","doi":"10.1117/1.JMI.11.4.044505","DOIUrl":"10.1117/1.JMI.11.4.044505","url":null,"abstract":"<p><strong>Purpose: </strong>Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1) outlines the exterior boundary of adnexal masses and (2) separates internal components.</p><p><strong>Approach: </strong>A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline ( <math> <mrow> <msub><mrow><mi>R</mi></mrow> <mrow><mi>HD</mi> <mtext>-</mtext> <mi>D</mi></mrow> </msub> </mrow> </math> ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components.</p><p><strong>Results: </strong>The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], and <math> <mrow> <msub><mrow><mi>R</mi></mrow> <mrow><mi>HD</mi> <mtext>-</mtext> <mi>D</mi></mrow> </msub> </mrow> </math> was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics.</p><p><strong>Conclusion: </strong>A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11301525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903209","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":"Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images.","authors":"Devi S Klein, Srijita Karmakar, Aditya Jonnalagadda, Craig K Abbey, Miguel P Eckstein","doi":"10.1117/1.JMI.11.4.045501","DOIUrl":"10.1117/1.JMI.11.4.045501","url":null,"abstract":"<p><strong>Purpose: </strong>Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors.</p><p><strong>Approach: </strong>Sixteen nonexpert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC).</p><p><strong>Results: </strong>The CNN-CADe improved the 3D search for the small microcalcification signal ( <math><mrow><mi>Δ</mi> <mtext> </mtext> <mi>AUC</mi> <mo>=</mo> <mn>0.098</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.0002</mn></mrow> </math> ) and the 2D search for the large mass signal ( <math><mrow><mi>Δ</mi> <mtext> </mtext> <mi>AUC</mi> <mo>=</mo> <mn>0.076</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.002</mn></mrow> </math> ). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D ( <math><mrow><mi>Δ</mi> <mi>Δ</mi> <mtext> </mtext> <mi>AUC</mi> <mo>=</mo> <mn>0.066</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.035</mn></mrow> </math> ). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe ( <math><mrow><mi>r</mi> <mo>=</mo> <mo>-</mo> <mn>0.528</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.036</mn></mrow> </math> ). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit ( <math><mrow><mi>Δ</mi> <mi>Δ</mi> <mtext> </mtext> <mi>AUC</mi> <mo>=</mo> <mn>0.033</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.133</mn></mrow> </math> ).</p><p><strong>Conclusion: </strong>The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11232702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581238","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":"Transformation from hematoxylin-and-eosin staining to Ki-67 immunohistochemistry digital staining images using deep learning: experimental validation on the labeling index.","authors":"Cunyuan Ji, Kengo Oshima, Takumi Urata, Fumikazu Kimura, Keiko Ishii, Takeshi Uehara, Kenji Suzuki, Saori Takeyama, Masahiro Yamaguchi","doi":"10.1117/1.JMI.11.4.047501","DOIUrl":"10.1117/1.JMI.11.4.047501","url":null,"abstract":"<p><strong>Purpose: </strong>Endometrial cancer (EC) is one of the most common types of cancer affecting women. While the hematoxylin-and-eosin (H&E) staining remains the standard for histological analysis, the immunohistochemistry (IHC) method provides molecular-level visualizations. Our study proposes a digital staining method to generate the hematoxylin-3,3'-diaminobenzidine (H-DAB) IHC stain of Ki-67 for the whole slide image of the EC tumor from its H&E stain counterpart.</p><p><strong>Approach: </strong>We employed a color unmixing technique to yield stain density maps from the optical density (OD) of the stains and utilized the U-Net for end-to-end inference. The effectiveness of the proposed method was evaluated using the Pearson correlation between the digital and physical stain's labeling index (LI), a key metric indicating tumor proliferation. Two different cross-validation schemes were designed in our study: intraslide validation and cross-case validation (CCV). In the widely used intraslide scheme, the training and validation sets might include different regions from the same slide. The rigorous CCV validation scheme strictly prohibited any validation slide from contributing to training.</p><p><strong>Results: </strong>The proposed method yielded a high-resolution digital stain with preserved histological features, indicating a reliable correlation with the physical stain in terms of the Ki-67 LI. In the intraslide scheme, using intraslide patches resulted in a biased accuracy (e.g., <math><mrow><mi>R</mi> <mo>=</mo> <mn>0.98</mn></mrow> </math> ) significantly higher than that of CCV. The CCV scheme retained a fair correlation (e.g., <math><mrow><mi>R</mi> <mo>=</mo> <mn>0.66</mn></mrow> </math> ) between the LIs calculated from the digital stain and its physical IHC counterpart. Inferring the OD of the IHC stain from that of the H&E stain enhanced the correlation metric, outperforming that of the baseline model using the RGB space.</p><p><strong>Conclusions: </strong>Our study revealed that molecule-level insights could be obtained from H&E images using deep learning. Furthermore, the improvement brought via OD inference indicated a possible method for creating more generalizable models for digital staining via per-stain analysis.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861255","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":"Generative adversarial network-based reconstruction of healthy anatomy for anomaly detection in brain CT scans.","authors":"Sina Walluscheck, Annika Gerken, Ivana Galinovic, Kersten Villringer, Jochen B Fiebach, Jan Klein, Stefan Heldmann","doi":"10.1117/1.JMI.11.4.044508","DOIUrl":"10.1117/1.JMI.11.4.044508","url":null,"abstract":"<p><strong>Purpose: </strong>To help radiologists examine the growing number of computed tomography (CT) scans, automatic anomaly detection is an ongoing focus of medical imaging research. Radiologists must analyze a CT scan by searching for any deviation from normal healthy anatomy. We propose an approach to detecting abnormalities in axial 2D CT slice images of the brain. Although much research has been done on detecting abnormalities in magnetic resonance images of the brain, there is little work on CT scans, where abnormalities are more difficult to detect due to the low image contrast that must be represented by the model used.</p><p><strong>Approach: </strong>We use a generative adversarial network (GAN) to learn normal brain anatomy in the first step and compare two approaches to image reconstruction: training an encoder in the second step and using iterative optimization during inference. Then, we analyze the differences from the original scan to detect and localize anomalies in the brain.</p><p><strong>Results: </strong>Our approach can reconstruct healthy anatomy with good image contrast for brain CT scans. We obtain median Dice scores of 0.71 on our hemorrhage test data and 0.43 on our test set with additional tumor images from publicly available data sources. We also compare our models to a state-of-the-art autoencoder and a diffusion model and obtain qualitatively more accurate reconstructions.</p><p><strong>Conclusions: </strong>Without defining anomalies during training, a GAN-based network was used to learn healthy anatomy for brain CT scans. Notably, our approach is not limited to the localization of hemorrhages and tumors and could thus be used to detect structural anatomical changes and other lesions.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917780","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":"Improving radiological quantification of levator hiatus features with measures informed by statistical shape modeling.","authors":"Vincenzia S Vargo, Megan R Routzong, Pamela A Moalli, Ghazaleh Rostaminia, Steven D Abramowitch","doi":"10.1117/1.JMI.11.4.045001","DOIUrl":"10.1117/1.JMI.11.4.045001","url":null,"abstract":"<p><strong>Purpose: </strong>The measures that traditionally describe the levator hiatus (LH) are straightforward and reliable; however, they were not specifically designed to capture significant differences. Statistical shape modeling (SSM) was used to quantify LH shape variation across reproductive-age women and identify novel variables associated with LH size and shape.</p><p><strong>Approach: </strong>A retrospective study of pelvic MRIs from 19 nulliparous, 32 parous, and 12 pregnant women was performed. The LH was segmented in the plane of minimal LH dimensions. SSM was implemented. LH size was defined by the cross-sectional area, maximal transverse diameter, and anterior-posterior (A-P) diameter. Novel SSM-guided variables were defined by regions of greatest variation. Multivariate analysis of variance (MANOVA) evaluated group differences, and correlations determined relationships between size and shape variables.</p><p><strong>Results: </strong>Overall shape ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), SSM mode 2 (oval to <math><mrow><mi>T</mi></mrow> </math> -shape, <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.002</mn></mrow> </math> ), mode 3 (rounder to broader anterior shape, <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.004</mn></mrow> </math> ), and maximal transverse diameter ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.003</mn></mrow> </math> ) significantly differed between groups. Novel anterior and posterior transverse diameters were identified at 14% and 79% of the A-P length. Anterior transverse diameter and maximal transverse diameter were strongly correlated ( <math><mrow><mi>r</mi> <mo>=</mo> <mn>0.780</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), while posterior transverse diameter and maximal transverse diameter were weakly correlated ( <math><mrow><mi>r</mi> <mo>=</mo> <mn>0.398</mn></mrow> </math> , <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.001</mn></mrow> </math> ).</p><p><strong>Conclusions: </strong>The traditional maximal transverse diameter generally corresponded with SSM findings but cannot describe anterior and posterior variation independently. The novel anterior and posterior transverse diameters represent both size and shape variation, can be easily calculated alongside traditional measures, and are more sensitive to subtle and local LH variation. Thus, they have a greater ability to serve as predictive and diagnostic parameters.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917781","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}