A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation method employs multi-modality atlases from MRI and CT and adopts a new label fusion algorithm which is based on the proposed multi-scale patch (MSP) strategy and a new global atlas ranking scheme. MSP, de...
first‐pass perfusionsimultaneous multislicespiral trajectoryTo develop and evaluate a simultaneous multislice (SMS) spiral perfusion pulse sequence with whole-heart coverage.An orthogonal set of phase cycling angles following a Hadamard pattern was incorporated into a golden-angle (GA) variable density ...
Finding the commonalities and variabilities within a system, and expressing them, forms the heart of design. Commonalities are often the parts that are difficult to explicitly identify, not because we don’t recognize them, but because they’re so easily and intuitively recognizable it’s tough ...
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015). Article PubMed PubMed Central Google Scholar Kleesiek, J. et al. Deep mri brain extraction: a 3d convolutional neural network for skull stripping. NeuroImage 129, ...
Multi-scale patch Whole heart segmentation Multi-modality atlas Local atlas ranking a b s t r a c t A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation method em- ploys multi-modality atlases from MRI and CT and adopts a new label fusion algorithm which...
while this procedure requires great attention to detail and expertize in anatomy and the imaging modality, it is highly repetitive and time-consuming. This limits the sample size that can be analyzed with justifiable efforts. To reduce the time needed for manual segmentation, interactive, and semi...
ModalityTraining setValidation setTest set Empty CellThick-sliceThin-sliceThick-sliceThin-sliceThick-sliceThin-slice MRI 810 1,303 203 326 189 982 CT 2,088 2,076 523 519 309 492 4.2.2. Data standardisation, pre-processing and augmentation For the pre-processing of these data, we normalised ...
This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the ...
The proposed 1D-segmentation model was trained and evaluated using 5-fold cross-validation, which ensured the reliability and robustness of the proposed model. We used five well-established performance metrics to comprehensively assess and compare the denoising performance of each of the five 1D-...
This indicates that for semantic segmentation and body part segmentation, respectively, cross-modality transfer learning is possible and can help to achieve better results. Nevertheless, it would be desirable to have a larger LWIR image dataset of infants/adults. In addition to transfer learning, ...