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Item type:Publication, Access status: without files , Dose robustness of deep learning models for anatomic segmentation of computed tomography imagesPurpose: The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations. Approach: We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibility with existing denoising methods is considered. Results: The results reveal the surprising robustness of the TotalSegmentator approach, showing minimal differences at the pixel level even without denoising. Less robust models show good compatibility with the denoising methods, which help to improve robustness in almost all cases. With denoising based on a convolutional neural network (CNN), the median Dice between low- and full-dose data does not fall below 0.9 (12 for the Hausdorff distance) for all but one model. We observe volatile results for labels with effective radii less than 19 mm and improved results for contrasted CT acquisitions. Conclusion: The proposed approach facilitates clinically relevant analysis of dose robustness for human organ segmentation models. The results outline the robustness properties of a diverse set of models. Further studies are needed to identify the robustness of approaches for lesion segmentation and to rank the factors contributing to dose robustness.Publicationtype: Journal Article8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning CT Segmentation from Label Masks Only(2024-04-27); ; ; ; Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.Publicationtype: Conference Paper28 55 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On TotalSegmentator’s performance on low-dose CT imagesDeep neural networks have emerged as the preferred method for semantic segmentation of CT images in recent years. However, understanding their limitations and generalization properties remains an active area of research and a relevant topic for clinical applications. One crucial factor among many is the X-ray radiation dose, which is always kept as low as reasonably possible during CT acquisition. Therefore, potential dose reductions may pose a challenge for existing segmentation models. In this paper, we investigate robustness of the recently proposed TotalSegmentator model for anatomical segmentation with respect to dose reduction. TotalSegmentator combines a large CT dataset and the well-established nnU-Net framework to train deep learning models, resulting in state-of-the-art performance for anatomical segmentation. Our method relies on accurate low-dose simulations derived from acquired full-dose projections. For a set of registered low- and full-dose CT images, we measure the Dice score between the corresponding segmentations. Our results reveal a high level of robustness in the segmentation outcomes. Comprehensive quantitative comparisons demonstrate that at a 20% dose level, the Dice score declines by at most 3%. Visual comparisons reveal only minor differences at the boundaries of the segmented organs. These findings may have a large potential for dose reduction when CT data are acquired predominantly for segmentation purposes, such as for the planning of interventional or surgical procedures.Publicationtype: Conference Paper38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Learning metal artifact reduction in cardiac CT images with moving pacemakersMetal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are not applicable. We propose a fully automatic Dynamic Pacemaker Artifact Reduction (DyPAR+) pipeline which is built of three convolutional neural network (CNN) ensembles. In a first step, pacemaker metal shadows are segmented directly in the raw projection data by the SegmentationNets. Second, resulting metal shadow masks are passed to the InpaintingNets which replace metal-affected line integrals in the sinogram for subsequent reconstruction of a metal-free image volume. Third, the metal locations in a pre-selected motion state are predicted by the ReinsertionNets based on a stack of partial angle back-projections generated from the segmented metal shadow mask. We generate the data required for the supervised learning processes by introducing synthetic, moving pacemaker leads into 14 clinical cases without pacemakers. The SegmentationNets and the ReinsertionNets achieve average Dice coefficients of 94.16% ± 2.01% and 55.60% ± 4.79% during testing on clinical data with synthetic metal leads. With a mean absolute reconstruction error of 11.54 HU ± 2.49 HU in the image domain, the InpaintingNets outperform the hand-crafted approaches PatchMatch and inverse distance weighting. Application of the proposed DyPAR+ pipeline to nine clinical test cases with real pacemakers leads to significant reduction of metal artifacts and demonstrates the transferability to clinical practice. Especially the SegmentationNets and InpaintingNets generalize well to unseen acquisition modes and contrast protocols.Publicationtype: Journal Article118 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Motion estimation and correction in cardiac CT angiography images using convolutional neural networks(2019-09); ; ; ; Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel motion compensation approach dealing with Coronary Motion estimation by Patch Analysis in CT data (CoMPACT) is presented. First, the required data for supervised learning is generated by the Coronary Motion Forward Artifact model for CT data (CoMoFACT) which introduces simulated motion to 19 artifact-free clinical CT cases with step-and-shoot acquisition protocol. Second, convolutional neural networks (CNNs) are trained to estimate underlying 2D motion vectors from 2.5D image patches based on the coronary artifact appearance. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion magnitude with average test accuracies of 13.37°±1.21° and 0.77 ± 0.09 mm, respectively. On clinical data with simulated motion, average test accuracies of 34.85°±2.09° and 1.86 ± 0.11 mm are achieved, whereby the precision of the motion direction prediction increases with the motion magnitude. The trained CNNs are integrated into an iterative motion compensation pipeline which includes distance-weighted motion vector extrapolation. Alternating motion estimation and compensation in twelve clinical cases with real cardiac motion artifacts leads to significantly reduced artifact levels, especially in image data with severe artifacts. In four observer studies, mean artifact levels of 3.08 ± 0.24 without MC and 2.28 ± 0.29 with CoMPACT MC are rated in a five point Likert scale.Publicationtype: Journal Article151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dynamic pacemaker artifact removal (DyPAR) from CT data using CNNs(Microtome Publishing, 2019-07); ; ; 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks(2019-02); ; ; ; Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures. A Coronary Motion Forward Artifact model for CT data (CoMoFACT) is developed and applied to clinical cases with excellent image quality to introduce motion artifacts using simulated motion vector fields. The data required for supervised learning is generated by the CoMoFACT from 17 prospectively ECG-triggered clinical cases with controlled motion levels on a scale of 0-10. Convolutional neural networks achieve an accuracy of 93.3% ± 1.8% for the classification task of separating motion-free from motion-perturbed coronary cross-sectional image patches. The target motion level is predicted by a corresponding regression network with a mean absolute error of 1.12 ± 0.07. Transferability and generalization capabilities are demonstrated by motion artifact measurements on eight additional CCTA cases with real motion artifacts.Publicationtype: Journal Article310 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Deep-learning-based CT motion artifact recognition in coronary arteries(2018); ; ; ; Downloading of the abstract is permitted for personal use only. The detection and subsequent correction of motion artifacts is essential for the high diagnostic value of non- invasive coronary angiography using cardiac CT. However, motion correction algorithms have a substantial computational footprint and possible failure modes which warrants a motion artifact detection step to decide whether motion correction is required in the first place. We investigate how accurately motion artifacts in the coronary arteries can be predicted by deep learning approaches. A forward model simulating cardiac motion by creating and integrating artificial motion vector fields in the filtered back projection (FBP) algorithm allows us to generate training data from nine prospectively ECG-triggered high quality clinical cases. We train a Convolutional Neural Network (CNN) classifying 2D motion-free and motion-perturbed coronary cross-section images and achieve a classification accuracy of 94:4% ± 2:9% by four-fold cross-validation.Publicationtype: Conference Paper207