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DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
TL;DR: In this article, an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor was proposed for coronary artery centerline extraction.
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Abstract: A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simultaneously computes centerline distance maps and detects branch endpoints. The method generates single-pixel-wide centerlines with no spurious branches. It handles arbitrary tree-structured object with no prior assumption regarding depth of the tree or its bifurcation pattern. It is also robust to substantial scale changes across different parts of the target object and minor imperfections of the object's segmentation mask. To the best of our knowledge, this is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object. The proposed method is validated in coronary artery centerline extraction on a dataset of 620 patients (400 of which used as test set). This application is challenging due to the large number of coronary branches, branch tortuosity, and large variations in length, thickness, shape, etc. The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask. Compared to a state-of-the-art traditional minimal path approach, our method improves patient-level success rate of centerline extraction from 54.3% to 88.8% according to independent human expert review.
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou,Hayit Greenspan,Christos Davatzikos,James S. Duncan,Bram van Ginneken,Anant Madabhushi,Jerry L. Prince,Daniel Rueckert,Ronald M. Summers +8 more
TL;DR: This survey article presents traits of medical imaging, highlights both clinical needs and technical challenges in medical Imaging, and describes how emerging trends in DL are addressing these issues, including the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on.
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Deep Learning for Cardiac Image Segmentation: A Review.
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria and vessels).
Learning tree-structured representation for 3D coronary artery segmentation.
TL;DR: A novel tree-structured convolutional gated recurrent unit (ConvGRU) model is proposed to learn the anatomical structure of the coronary artery, which considers the local spatial correlations in the input data as the convolutions are used for input-to-state as well as state- to-state transitions, thus more suitable for image analysis.
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Artificial intelligence: improving the efficiency of cardiovascular imaging
TL;DR: In this article, the authors describe the use of computational techniques to mimic human intelligence in healthcare, which typically involves large medical datasets being used to predict a diagnos... and this typically involves a large number of patients to be treated.
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Geometrical and deep learning approaches for instance segmentation of CFRP fiber bundles in textile composites
TL;DR: Two new methodologies for splitting tow instances are proposed, based on the geometrical analysis of the material structure using conventional image analysis and a deep learning-based method trained using randomly generated synthetic images of a woven composite material that avoids an expensive human annotation step.
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TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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Progressive Attention Guided Recurrent Network for Salient Object Detection
Xiaoning Zhang,Tiantian Wang,Jinqing Qi,Huchuan Lu,Gang Wang +4 more
- 18 Jun 2018
TL;DR: A novel attention guided network which selectively integrates multi-level contextual information in a progressive manner and introduces multi-path recurrent feedback to enhance this proposed progressive attention driven framework.
Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach
TL;DR: The presented results show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.
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Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches.
Yefeng Zheng,Huseyin Tek,Gareth Funka-Lea +2 more
- 22 Sep 2013
TL;DR: The automatically segmented chambers are exploited to predict the initial position of the major coronary centerlines and define a vessel-specific region-of-interest (ROI) to constrain the following centerline refinement.