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4D CNN for semantic segmentation of cardiac volumetric sequences
[article]
2019
arXiv
pre-print
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We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. ...
We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA. ...
For sequences of volumetric imaging, such as 3D+T CT or ultrasound, 4D CNNs are a natural extension. Wang et al. ...
arXiv:1906.07295v2
fatcat:g66sjzitazayfehmjlwy5ccdnu
Deep Learning in Spatiotemporal Cardiac Imaging: A Review of Methodologies and Clinical Usability
2020
Computers in Biology and Medicine
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This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of ...
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. ...
[48] presented an approach for volumetric semantic segmentation of the LV and LV myocardium in cardiac sequences. ...
doi:10.1016/j.compbiomed.2020.104200
pmid:33421825
fatcat:ltxjpt6yhzgvdkifo4wo3ftveq
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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for Cardiac MR Image Sequences 681 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes 682 Real Time RNN Based 3D Ultrasound Scan Adequacy for Developmental Dysplasia ...
Volumetric Clipping Surface: Un-occluded visualization of structures preserving depth cues into surrounding organs 463 Tract orientation mapping for bundle-specific tractography 474 Computing CNN Loss ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey
[article]
2022
arXiv
pre-print
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At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. ...
Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. ...
analysis, and the use of 3D CNN for volumetric medical image segmentation further increases the complexity. ...
arXiv:2108.08467v3
fatcat:s2rzghycjbczpparmrflsdzujq
Convolutional module for heart localization and segmentation in MRI
[article]
2021
arXiv
pre-print
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Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. ...
We experimented and evaluated VMF on three CMR datasets, observing that the proposed ROIs cover 99.7% of data labels (Recall score), improved the CNN segmentation (mean Dice score) by 1.7 (p < .001) after ...
Conflict of interest: The authors declare that they have no conflict of interest. ...
arXiv:2107.09134v1
fatcat:msdr2zcwsjhpjj4xw3o6u6qwmu
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
[article]
2022
arXiv
pre-print
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Extensive experiments demonstrate the potential of UTNetV2 as a general segmentation backbone, outperforming CNNs and vision Transformers on three public datasets with multiple modalities (e.g., CT and ...
We make the data processing, models and evaluation pipeline publicly available, offering solid baselines and unbiased comparisons for promoting a wide range of downstream clinical applications. ...
The token map is flatten to a sequence as the input of the Transformer block: X ∈ R n×d (bold for flattened 1D sequence, while regular for 2D token map), where n = HW is the sequence length. ...
arXiv:2203.00131v3
fatcat:dmuh4yga4rahzjjdy4ttg7eei4
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
[article]
2018
arXiv
pre-print
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Our approach in ACDC-2017 challenge stands second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100%. ...
From the segmentation we extracted clinically relevant cardiac parameters and hand-crafted features which reflected the clinical diagnostic analysis to train an ensemble system for cardiac disease classification ...
Loss function In CNN based techniques, segmentation of volumetric medical images is achieved by performing voxel-wise classification. ...
arXiv:1801.05173v1
fatcat:bx547rpqeva6fk2qmddgdg46fe
Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
2021
Applied Sciences
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Automation of 3D BVS using deep supervised learning is being researched, and U-Net-based approaches, which are considered as standard for medical image segmentation, are proposed a lot. ...
Blood vessel segmentation (BVS) of 3D medical imaging such as computed tomography and magnetic resonance angiography (MRA) is an essential task in the clinical field. ...
(FCN) baseline, which is a type of CNN for semantic segmentation with a encoder-decoder structure [22] . ...
doi:10.3390/app11052014
fatcat:hjfnwrx5jzeexjmqj3kj5ucnj4
Deep Semantic Segmentation of Natural and Medical Images: A Review
[article]
2020
arXiv
pre-print
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Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation. ...
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. ...
similar performance to full supervision for segmentation of cardiac images. ...
arXiv:1910.07655v3
fatcat:uxrrmb3jofcsvnkfkuhfwi62yq
A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
[article]
2021
arXiv
pre-print
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Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. ...
be applied for efficiently constructing 4D whole heart dynamics. ...
Angjoo Kanazawa for her valuable comments concerning this work. ...
arXiv:2102.07899v1
fatcat:g6hnpblkzvfxbchm2pmj552cea
Image Segmentation Using Deep Learning: A Survey
[article]
2020
arXiv
pre-print
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In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully ...
Various algorithms for image segmentation have been developed in the literature. ...
ACKNOWLEDGMENTS The authors would like to thank Tsung-Yi Lin from Google Brain, and Jingdong Wang and Yuhui Yuan from Microsoft Research Asia, for reviewing this work, and providing very helpful comments ...
arXiv:2001.05566v5
fatcat:wiep26nijncwxjojxbzrqoonti
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
[article]
2021
arXiv
pre-print
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We analyse the impact of prescribed summary lengths and show experimental evidence for the effectiveness of 3DST-UNet-RL on two commonly used general video summarization benchmarks. ...
A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). ...
[41] proposed a 3D U-Net model that generate dense volumetric segmentations. It realizes 3D image segmentation by inputting a continuous 2D slice sequence of 3D images. ...
arXiv:2106.10528v1
fatcat:6q3wnioqtrdktkfl5w5n3pjthi
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
[article]
2019
arXiv
pre-print
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Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. ...
To achieve this goal, a set of training data is generally needed for constructing priors or for training. ...
Acknowledgement This work was funded in part by the Chinese NSFC research fund, the Science and Technology Commission of Shanghai Municipality (17JC1401600) and the British Heart Foundation Project Grant ...
arXiv:1902.07880v1
fatcat:7w3sp334ejdotc3p7h2uwjpewe
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
ArXiv was searched for papers mentioning one of a set of terms related to medical imaging. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
2019
Medical Image Analysis
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Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. ...
The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. ...
Acknowledgements This work was funded in part by the National Natural Science Foundation of China (NSFC) grant ( 61971142 ), the Science and Technology Commission of Shanghai Municipality grant ( 17JC1401600 ...
doi:10.1016/j.media.2019.101537
pmid:31446280
pmcid:PMC6839613
fatcat:4a5fbpvz5jgi3hyafow2pcpfw4
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