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Found 11 papers, 3 papers with code
Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation
For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively.
Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network
By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch.
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.
Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information
However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training.
Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction
We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images.
Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing
To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches.
Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation
In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists.
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint
Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches.
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images.
