Journal of Medical Imaging最新文献

{"title":"Demystifying the effect of receptive field size in U-Net models for medical image segmentation.","authors":"Vincent Loos, Rohit Pardasani, Navchetan Awasthi","doi":"10.1117/1.JMI.11.5.054004","DOIUrl":"10.1117/1.JMI.11.5.054004","url":null,"abstract":"<p><strong>Purpose: </strong>Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results in this domain. We delve into the understudied aspect of receptive field (RF) size and its impact on the U-Net and attention U-Net architectures used for medical imaging segmentation.</p><p><strong>Approach: </strong>We explore several critical elements including the relationship among RF size, characteristics of the region of interest, and model performance, as well as the balance between RF size and computational costs for U-Net and attention U-Net methods for different datasets. We also propose a mathematical notation for representing the theoretical receptive field (TRF) of a given layer in a network and propose two new metrics, namely, the effective receptive field (ERF) rate and the object rate, to quantify the fraction of significantly contributing pixels within the ERF against the TRF area and assessing the relative size of the segmentation object compared with the TRF size, respectively.</p><p><strong>Results: </strong>The results demonstrate that there exists an optimal TRF size that successfully strikes a balance between capturing a wider global context and maintaining computational efficiency, thereby optimizing model performance. Interestingly, a distinct correlation is observed between the data complexity and the required TRF size; segmentation based solely on contrast achieved peak performance even with smaller TRF sizes, whereas more complex segmentation tasks necessitated larger TRFs. Attention U-Net models consistently outperformed their U-Net counterparts, highlighting the value of attention mechanisms regardless of TRF size.</p><p><strong>Conclusions: </strong>These insights present an invaluable resource for developing more efficient U-Net-based architectures for medical imaging and pave the way for future exploration of other segmentation architectures. A tool is also developed, which calculates the TRF for a U-Net (and attention U-Net) model and also suggests an appropriate TRF size for a given model and dataset.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"054004"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548314","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":"HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.","authors":"Raissa Souza, Emma A M Stanley, Vedant Gulve, Jasmine Moore, Chris Kang, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D Forkert","doi":"10.1117/1.JMI.11.5.054502","DOIUrl":"10.1117/1.JMI.11.5.054502","url":null,"abstract":"<p><strong>Purpose: </strong>Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts. Although data harmonization can mitigate this issue, current methods typically rely on large or paired datasets, which can be impractical to obtain in distributed setups.</p><p><strong>Approach: </strong>We introduced HarmonyTM, a data harmonization method tailored for the TM. HarmonyTM effectively mitigates bias in the model's feature representation while retaining crucial disease-related information, all without requiring extensive datasets. Specifically, we employed adversarial training to \"unlearn\" bias from the features used in the model for classifying Parkinson's disease (PD). We evaluated HarmonyTM using multi-center three-dimensional (3D) neuroimaging datasets from 83 centers using 23 different scanners.</p><p><strong>Results: </strong>Our results show that HarmonyTM improved PD classification accuracy from 72% to 76% and reduced (unwanted) scanner classification accuracy from 53% to 30% in the TM setup.</p><p><strong>Conclusion: </strong>HarmonyTM is a method tailored for harmonizing 3D neuroimaging data within the TM approach, aiming to minimize shortcut learning in distributed setups. This prevents the disease classifier from leveraging scanner-specific details to classify patients with or without PD-a key aspect for deploying ML models for clinical applications.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"054502"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298698","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":"Expanding generalized contrast-to-noise ratio into a clinically relevant measure of lesion detectability by considering size and spatial resolution.","authors":"Siegfried Schlunk, Brett Byram","doi":"10.1117/1.JMI.11.5.057001","DOIUrl":"https://doi.org/10.1117/1.JMI.11.5.057001","url":null,"abstract":"<p><strong>Purpose: </strong>Early image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, invalidating the meaning of those metrics. The result is that beamformers may \"manipulate\" metrics without producing more clinical information.</p><p><strong>Approach: </strong>In this work, Smith et al.'s signal-to-noise ratio (SNR) metric for lesion detectability is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses generalized contrast-to-noise ratio (gCNR) as a core. It is analytically shown that for Rayleigh distributed data, gCNR is a function of Smith et al.'s <math> <mrow><msub><mi>C</mi> <mi>ψ</mi></msub> </mrow> </math> (and therefore can be used as a substitution). More robust methods for estimating the resolution cell size are considered. Simulated lesions are included to verify the equations and demonstrate behavior, and it is shown to apply equally well to <i>in vivo</i> data.</p><p><strong>Results: </strong>gSNR is shown to be equivalent to SNR for delay-and-sum (DAS) beamformed data, as intended. However, it is shown to be more robust against transformations and report lesion detectability more accurately for non-Rayleigh distributed data. In the simulation included, the SNR of DAS was <math><mrow><mn>4.4</mn> <mo>±</mo> <mn>0.8</mn></mrow> </math> , and minimum variance (MV) was <math><mrow><mn>6.4</mn> <mo>±</mo> <mn>1.9</mn></mrow> </math> , but the gSNR of DAS was <math><mrow><mn>4.5</mn> <mo>±</mo> <mn>0.9</mn></mrow> </math> , and MV was <math><mrow><mn>3.0</mn> <mo>±</mo> <mn>0.9</mn></mrow> </math> , which agrees with the subjective assessment of the image. Likewise, the <math> <mrow><msup><mi>DAS</mi> <mn>2</mn></msup> </mrow> </math> transformation (which is clinically identical to DAS) had an incorrect SNR of <math><mrow><mn>9.4</mn> <mo>±</mo> <mn>1.0</mn></mrow> </math> and a correct gSNR of <math><mrow><mn>4.4</mn> <mo>±</mo> <mn>0.9</mn></mrow> </math> . Similar results are shown <i>in vivo</i>.</p><p><strong>Conclusions: </strong>Using gCNR as a component to estimate gSNR creates a robust measure of lesion detectability. Like SNR, gSNR can be compared with the Rose criterion and may better correlate with clinical assessments of image quality for modern beamformers.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"057001"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11498315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510423","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 echocardiography view classification and quality assessment with recognition of unknown views.","authors":"Gino E Jansen, Bob D de Vos, Mitchel A Molenaar, Mark J Schuuring, Berto J Bouma, Ivana Išgum","doi":"10.1117/1.JMI.11.5.054002","DOIUrl":"10.1117/1.JMI.11.5.054002","url":null,"abstract":"<p><strong>Purpose: </strong>Interpreting echocardiographic exams requires substantial manual interaction as videos lack scan-plane information and have inconsistent image quality, ranging from clinically relevant to unrecognizable. Thus, a manual prerequisite step for analysis is to select the appropriate views that showcase both the target anatomy and optimal image quality. To automate this selection process, we present a method for automatic classification of routine views, recognition of unknown views, and quality assessment of detected views.</p><p><strong>Approach: </strong>We train a neural network for view classification and employ the logit activations from the neural network for unknown view recognition. Subsequently, we train a linear regression algorithm that uses feature embeddings from the neural network to predict view quality scores. We evaluate the method on a clinical test set of 2466 echocardiography videos with expert-annotated view labels and a subset of 438 videos with expert-rated view quality scores. A second observer annotated a subset of 894 videos, including all quality-rated videos.</p><p><strong>Results: </strong>The proposed method achieved an accuracy of <math><mrow><mn>84.9</mn> <mo>%</mo> <mo>±</mo> <mn>0.67</mn></mrow> </math> for the joint objective of routine view classification and unknown view recognition, whereas a second observer reached an accuracy of 87.6%. For view quality assessment, the method achieved a Spearman's rank correlation coefficient of 0.71, whereas a second observer reached a correlation coefficient of 0.62.</p><p><strong>Conclusion: </strong>The proposed method approaches expert-level performance, enabling fully automatic selection of the most appropriate views for manual or automatic downstream analysis.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"054002"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113461","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 network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.","authors":"Nagasoujanya V Annasamudram, Azubuike M Okorie, Richard G Spencer, Rita R Kalyani, Qi Yang, Bennett A Landman, Luigi Ferrucci, Sokratis Makrogiannis","doi":"10.1117/1.JMI.11.5.054003","DOIUrl":"10.1117/1.JMI.11.5.054003","url":null,"abstract":"<p><strong>Purpose: </strong>Segmentation is essential for tissue quantification and characterization in studies of aging and age-related and metabolic diseases and the development of imaging biomarkers. We propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in three-dimensional (3D) thigh magnetic resonance images. These groups lie anatomically adjacent to each other, rendering their manual delineation a challenging and time-consuming task.</p><p><strong>Approach: </strong>We introduce a framework for automated segmentation of the four main functional muscle groups of the thigh, gracilis, hamstring, quadriceps femoris, and sartorius, using chemical shift encoded water-fat magnetic resonance imaging (CSE-MRI). We propose fusing anatomical mappings from multiple deformable models with 3D deep learning model-based segmentation. This approach leverages the generalizability of multi-atlas segmentation (MAS) and accuracy of deep networks, hence enabling accurate assessment of volume and fat content of muscle groups.</p><p><strong>Results: </strong>For segmentation performance evaluation, we calculated the Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD-95). We evaluated the proposed framework, its variants, and baseline methods on 15 healthy subjects by threefold cross-validation and tested on four patients. Fusion of multiple atlases, deformable registration models, and deep learning segmentation produced the top performance with an average DSC of 0.859 and HD-95 of 8.34 over all muscles.</p><p><strong>Conclusions: </strong>Fusion of multiple anatomical mappings from multiple MAS techniques enriches the template set and improves the segmentation accuracy. Additional fusion with deep network decisions applied to the subject space offers complementary information. The proposed approach can produce accurate segmentation of individual muscle groups in 3D thigh MRI scans.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"054003"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11369361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134214","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":"Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.","authors":"Karen Drukker, Milica Medved, Carla B Harmath, Maryellen L Giger, Obianuju S Madueke-Laveaux","doi":"10.1117/1.JMI.11.5.054501","DOIUrl":"https://doi.org/10.1117/1.JMI.11.5.054501","url":null,"abstract":"<p><strong>Significance: </strong>Uterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity.</p><p><strong>Aim: </strong>We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity.</p><p><strong>Approach: </strong>We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score.</p><p><strong>Results: </strong>The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of <math><mrow><mn>0.93</mn> <mtext>  </mtext> <msup><mrow><mi>cm</mi></mrow> <mrow><mn>3</mn></mrow> </msup> <mo>/</mo> <mi>year</mi> <mo>/</mo> <mi>fibroid</mi></mrow> </math> from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility.</p><p><strong>Conclusion: </strong>We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"054501"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11391479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298699","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":"Photon-counting computed tomography versus energy-integrating computed tomography for detection of small liver lesions: comparison using a virtual framework imaging.","authors":"Nicholas Felice, Benjamin Wildman-Tobriner, William Paul Segars, Mustafa R Bashir, Daniele Marin, Ehsan Samei, Ehsan Abadi","doi":"10.1117/1.JMI.11.5.053502","DOIUrl":"10.1117/1.JMI.11.5.053502","url":null,"abstract":"<p><strong>Purpose: </strong>Photon-counting computed tomography (PCCT) has the potential to provide superior image quality to energy-integrating CT (EICT). We objectively compare PCCT to EICT for liver lesion detection.</p><p><strong>Approach: </strong>Fifty anthropomorphic, computational phantoms with inserted liver lesions were generated. Contrast-enhanced scans of each phantom were simulated at the portal venous phase. The acquisitions were done using DukeSim, a validated CT simulation platform. Scans were simulated at two dose levels ( <math> <mrow> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> 1.5 to 6.0 mGy) modeling PCCT (NAEOTOM Alpha, Siemens, Erlangen, Germany) and EICT (SOMATOM Flash, Siemens). Images were reconstructed with varying levels of kernel sharpness (soft, medium, sharp). To provide a quantitative estimate of image quality, the modulation transfer function (MTF), frequency at 50% of the MTF ( <math> <mrow><msub><mi>f</mi> <mn>50</mn></msub> </mrow> </math> ), noise magnitude, contrast-to-noise ratio (CNR, per lesion), and detectability index ( <math> <mrow> <msup><mrow><mi>d</mi></mrow> <mrow><mo>'</mo></mrow> </msup> </mrow> </math> , per lesion) were measured.</p><p><strong>Results: </strong>Across all studied conditions, the best detection performance, measured by <math> <mrow> <msup><mrow><mi>d</mi></mrow> <mrow><mo>'</mo></mrow> </msup> </mrow> </math> , was for PCCT images with the highest dose level and softest kernel. With soft kernel reconstruction, PCCT demonstrated improved lesion CNR and <math> <mrow> <msup><mrow><mi>d</mi></mrow> <mrow><mo>'</mo></mrow> </msup> </mrow> </math> compared with EICT, with a mean increase in CNR of 35.0% ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and 21% ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and a mean increase in <math> <mrow> <msup><mrow><mi>d</mi></mrow> <mrow><mo>'</mo></mrow> </msup> </mrow> </math> of 41.0% ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and 23.3% ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.007</mn></mrow> </math> ) for the 1.5 and 6.0 mGy acquisitions, respectively. The improvements were greatest for larger phantoms, low-contrast lesions, and low-dose scans.</p><p><strong>Conclusions: </strong>PCCT demonstrated objective improvement in liver lesion detection and image quality metrics compared with EICT. These advances may lead to earlier and more accurate liver lesion detection, thus improving patient care.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"053502"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11486217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477766","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":"ChatGP-Me?","authors":"Elias Levy, Bennett Landman","doi":"10.1117/1.JMI.11.5.050101","DOIUrl":"https://doi.org/10.1117/1.JMI.11.5.050101","url":null,"abstract":"<p><p>The editorial evaluates how the GenAI technologies available in 2024 (without specific coding) could impact scientific processes, exploring two AI tools with the aim of demonstrating what happens when using custom LLMs in five research lab workflows.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 5","pages":"050101"},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11513651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548313","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 single-shot propagation and speckle based phase recovery techniques for object thickness estimation by using a polychromatic X-ray laboratory source.","authors":"Diego Rosich, Margarita Chevalier, Adrián Belarra, Tatiana Alieva","doi":"10.1117/1.JMI.11.4.043501","DOIUrl":"10.1117/1.JMI.11.4.043501","url":null,"abstract":"<p><strong>Purpose: </strong>Propagation and speckle-based techniques allow reconstruction of the phase of an X-ray beam with a simple experimental setup. Furthermore, their implementation is feasible using low-coherence laboratory X-ray sources. We investigate different approaches to include X-ray polychromaticity for sample thickness recovery using such techniques.</p><p><strong>Approach: </strong>Single-shot Paganin (PT) and Arhatari (AT) propagation-based and speckle-based (ST) techniques are considered. The radiation beam polychromaticity is addressed using three different averaging approaches. The emission-detection process is considered for modulating the X-ray beam spectrum. Reconstructed thickness of three nylon-6 fibers with diameters in the millimeter-range, placed at various object-detector distances are analyzed. In addition, the thickness of an in-house made breast phantom is recovered by using multi-material Paganin's technique (MPT) and compared with micro-CT data.</p><p><strong>Results: </strong>The best quantitative result is obtained for the PT and ST combined with sample thickness averaging (TA) approach that involves individual thickness recovery for each X-ray spectral component and the smallest considered object-detector distance. The error in the recovered fiber diameters for both techniques is <math><mrow><mo><</mo> <mn>4</mn> <mo>%</mo></mrow> </math> , despite the higher noise level in ST images. All cases provide estimates of fiber diameter ratios with an error of 3% with respect to the nominal diameter ratios. The breast phantom thickness difference between MPT-TA and micro-CT is about 10%.</p><p><strong>Conclusions: </strong>We demonstrate the single-shot PT-TA and ST-TA techniques feasibility for thickness recovery of millimeter-sized samples using polychromatic microfocus X-ray sources. The application of MPT-TA for thicker and multi-material samples is promising.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"043501"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789482","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":"Projected pooling loss for red nucleus segmentation with soft topology constraints.","authors":"Guanghui Fu, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabregue, Stéphane Lehéricy, Olivier Colliot","doi":"10.1117/1.JMI.11.4.044002","DOIUrl":"10.1117/1.JMI.11.4.044002","url":null,"abstract":"<p><strong>Purpose: </strong>Deep learning is the standard for medical image segmentation. However, it may encounter difficulties when the training set is small. Also, it may generate anatomically aberrant segmentations. Anatomical knowledge can be potentially useful as a constraint in deep learning segmentation methods. We propose a loss function based on projected pooling to introduce soft topological constraints. Our main application is the segmentation of the red nucleus from quantitative susceptibility mapping (QSM) which is of interest in parkinsonian syndromes.</p><p><strong>Approach: </strong>This new loss function introduces soft constraints on the topology by magnifying small parts of the structure to segment to avoid that they are discarded in the segmentation process. To that purpose, we use projection of the structure onto the three planes and then use a series of MaxPooling operations with increasing kernel sizes. These operations are performed both for the ground truth and the prediction and the difference is computed to obtain the loss function. As a result, it can reduce topological errors as well as defects in the structure boundary. The approach is easy to implement and computationally efficient.</p><p><strong>Results: </strong>When applied to the segmentation of the red nucleus from QSM data, the approach led to a very high accuracy (Dice 89.9%) and no topological errors. Moreover, the proposed loss function improved the Dice accuracy over the baseline when the training set was small. We also studied three tasks from the medical segmentation decathlon challenge (MSD) (heart, spleen, and hippocampus). For the MSD tasks, the Dice accuracies were similar for both approaches but the topological errors were reduced.</p><p><strong>Conclusions: </strong>We propose an effective method to automatically segment the red nucleus which is based on a new loss for introducing topology constraints in deep learning segmentation.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044002"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11232703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581240","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}