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Found 12 papers, 4 papers with code
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis.
Deep vessel segmentation with joint multi-prior encoding
The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree.
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments.
Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models.
Cross-modal tumor segmentation using generative blending augmentation and self training
\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions.
Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
Extensive experiments with simulated and real computed tomography (CT) data were performed to validate the effectiveness of the proposed methods and we reported increased reconstruction accuracy compared to CAOL and iterative methods with single and joint total-variation (TV) regularization.
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Regularized directional representations for medical image registration
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion.
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies.
DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks
The task of medical image reconstruction involves mapping of projection main data collected from the detector to the image domain.
Computed Tomography (CT)
Denoising
+2
Medical Physics
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
Features were more reliable in PET with quantizationB, whereas quantizationW showed better results in CT. Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly amongst metrics.
