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Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. Expand
Optimal Multiple Surface Segmentation With Shape and Context Priors
A novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges is reported. Expand
Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information
A novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. Expand
Error-Tolerant Scribbles Based Interactive Image Segmentation
The proposed ratio energy function aims to minimize the graph-cut energy while maximizing the user input respected in the segmentation and preserves the "anchoring" capability of the userinput. Expand
Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs
  • Dan Wu, Xin Wang, +7 authors Youbing Yin
  • Computer Science, Medicine
  • International Journal of Computer Assisted…
  • 27 November 2018
The TreeLab-Net is able to capture the characteristics of tree structures by learning the spatial and topological dependencies of blood vessels effectively and achieves higher F1 scores with less topological errors. Expand
MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images
The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. Expand
Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
The proposed CNN-RNN deep learning framework was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow. Expand
Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging
This study proposes a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images and demonstrates the effectiveness of the framework and its superiority to seven FFR computation methods based on machine learning. Expand
Surface-Region Context in Optimal Multi-object Graph-Based Segmentation: Robust Delineation of Pulmonary Tumors
A novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field, is reported. Expand
Multi-scale segmentation using deep graph cuts: Robust lung tumor delineation in MVCBCT
This paper formulate the multi-scale segmentation as a Markov Random Field energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. Expand