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Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features
An automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes is proposed and an efficient and robust approach for automatic heart chamber segmentation in 3D CT volumes is developed. Expand
Robust point matching for nonrigid shapes by preserving local neighborhood structures
  • Yefeng Zheng, D. Doermann
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 April 2006
This paper introduces the notion of a neighborhood structure for the general point matching problem, and forms point matching as an optimization problem to preserve local neighborhood structures during matching. Expand
Script-Independent Text Line Segmentation in Freestyle Handwritten Documents
A novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method, which consistently outperforms previous methods on text line segmentation in freestyle handwritten documents. Expand
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
This work proposes a generic cross-modality synthesis approach and shows that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. Expand
Med3D: Transfer Learning for 3D Medical Image Analysis
A heterogeneous 3D network called Med3D is designed to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models which can accelerate the training convergence speed of target 3D medical tasks and improve accuracy ranging from 3% to 20%. Expand
Machine printed text and handwriting identification in noisy document images
This paper addresses the problem of the identification of text in noisy document images by treating noise as a separate class and model noise based on selected features. Expand
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
This work couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis, and significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective. Expand
X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks
This work proposes to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework, resulting in a high-quality CT volume both visually and quantitatively. Expand
Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets
A standardisation framework can be used to label and further analyse anatomical regions of the LA by performing the standardisation directly on the left atrial surface, including meshes exported from different electroanatomical mapping systems. Expand
Spine detection in CT and MR using iterated marginal space learning
A novel method is presented that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects to simultaneously detect and label the spinal disks. Expand