We extensively evaluated our method on the data set of the multi-modality whole heart segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The dice values of whole heart segmentation are 0.914 (CT images) and 0.890 (MRI images), which are both higher than the state-of-the-art.doi:10.1016/j.compmedimag....
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 ...
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...
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, ...
Multimodality imaging in cardiology, and particularly congenital heart disease, has evolved into a critical tool, essential for clinical decision-making and management. Understanding the strengths and weaknesses of each imaging modality allows for timely and accurate diagnosis, enables their complementary us...
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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, ...
Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data are often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefor
Li et al. [70] developed an improved 2D U-Net model for brain tumor segmentation using multimodal MRI. The model generates segmentation maps slice-by-slice and achieves higher segmentation performance compared to mono-modality. Wang et al. [71] proposed a 3D multitask CNN model to jointly ...
First, we show the performance of the model as a whole and give the segmentation results for the test dataset. Second, we compare CMP-UNet with some other retinal vessel segmentation methods proposed in recent years to verify the excellent performance of the model. Then, we conduct ablation ...