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3D FractalNet: Dense Volumetric Segmentation for Cardiovascular MRI Volumes [chapter]

Lequan Yu, Xin Yang, Jing Qin, Pheng-Ann Heng
2017 Lecture Notes in Computer Science  
Cardiac image segmentation plays a crucial role and even a prerequisite for diverse medical applications.  ...  The proposed 3D fractal network takes advantage of fully convolutional architecture to perform efficient, precise, volume-to-volume prediction.  ...  The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (Project no. CUHK 412513).  ... 
doi:10.1007/978-3-319-52280-7_10 fatcat:2wl4etclzbdhjotssjbco5jwlu

An application of cascaded 3D fully convolutional networks for medical image segmentation

Holger R. Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
2018 Computerized Medical Imaging and Graphics  
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images.  ...  Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results.  ...  Conflict of interest statement: The authors declare that they have no conflict of interest. References  ... 
doi:10.1016/j.compmedimag.2018.03.001 pmid:29573583 fatcat:6ihzswvpurdfval676ptnebv4q

Hierarchical 3D fully convolutional networks for multi-organ segmentation [article]

Holger R. Roth, Hirohisa Oda, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
2017 arXiv   pre-print
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images.  ...  segmentation results, while avoiding the need for handcrafting features or training organ-specific models.  ...  Abstract Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images.  ... 
arXiv:1704.06382v1 fatcat:hho3uvdhnbarfdpmeiqzgovsxu

`Project Excite' Modules for Segmentation of Volumetric Medical Scans [article]

Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger
2019 arXiv   pre-print
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.  ...  So far, the development of SE has focused on 2D images. In this paper, we propose 'Project & Excite' (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images.  ...  Introduction Fully convolutional neural networks (F-CNNs) have been widely adopted for semantic image segmentation in computer vision [4] and medical imaging [5] .  ... 
arXiv:1906.04649v2 fatcat:5ip2it4agvebrjy65daoq6a2xi

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Kai Niu, Xueyan Li, Li Zhang, Zhensong Yan, Wei Yu, Peipeng Liang, Yan Wang, Ching-Po Lin, Huimao Zhang, Chunjie Guo, Kuncheng Li, Tianyi Qian
2021 Quantitative Imaging in Medicine and Surgery  
The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted  ...  Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV).  ...  transfer and a SLANT-27 been designed for the accurate segmentation of medical segmentation module to reduce the variability effects of © Quantitative Imaging in Medicine and Surgery  ... 
doi:10.21037/qims-21-653 pmid:35284270 pmcid:PMC8899955 fatcat:rio22psyj5ghlare6a6nlpujue

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation [article]

Hao Chen, Qi Dou, Lequan Yu, Pheng-Ann Heng
2016 arXiv   pre-print
However, how to fully leverage contextual representations for recognition tasks from volumetric data has not been well studied, especially in the field of medical image computing, where a majority of image  ...  We believe this work unravels the potential of 3D deep learning to advance the recognition performance on volumetric image segmentation.  ...  However, in the field of medical image computing, volumetric data accounts for a large portion of medical image modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), etc.  ... 
arXiv:1608.05895v1 fatcat:xvg53rucyvgjrookcm73kgymxq

Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks [article]

Russell Bates, Benjamin Irving, Bostjan Markelc, Jakob Kaeppler, Ruth Muschel, Vicente Grau, Julia A. Schnabel
2017 arXiv   pre-print
We demonstrate the effectiveness of this hybrid convolutional-recurrent architecture over both 2D and 3D convolutional comparators.  ...  As such, considerable effort has been focused on the automated measurement and analysis of vasculature in medical and pre-clinical images.  ...  ACKNOWLEDGEMENTS The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA  ... 
arXiv:1705.09597v1 fatcat:63e2zjuknfdy5kblst4f4revyy

3D Deep Learning on Medical Images: A Review [article]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 arXiv   pre-print
In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images.  ...  We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization  ...  [34] presented a modified version of the original U-Net i.e., 3D U-Net for volumetric segmentation from sparse notation. 3D Medical Imaging Pre-Processing Preprocessing of the image dataset before  ... 
arXiv:2004.00218v4 fatcat:iucszcjffnbwbbzc4zzqpbvahy

Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, Future [article]

Teofilo E. Zosa
2021 arXiv   pre-print
deep learning architectures with historical background and the elucidation of the current trajectory of volumetric medical image segmentation research.  ...  Core to medical image analysis is the task of semantic segmentation which enables various clinical workflows.  ...  VOLUMETRIC SEGMENTATION NETWORKS Recent neural networks for volumetric segmentation in medical imaging can be roughly divided into those that work in 2D and those that work in 3D. 1 A. 2D vs. 3D The  ... 
arXiv:2103.14969v2 fatcat:ikxjpikwrneb3ijt6acerkdobu

Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT [article]

Hans Meine, Grzegorz Chlebus, Mohsen Ghafoorian, Itaru Endo, Andrea Schenk
2018 arXiv   pre-print
Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis of convolutional neural  ...  Using a set of 219 liver CT datasets with reference segmentations from liver surgery planning, we evaluate the performance of several neural network classifiers based on 2D and 3D U-net architectures.  ...  methods in medical image computing was on liver segmentation [3] .  ... 
arXiv:1810.04017v1 fatcat:ieoryhzulbbzzdjrufnpcfstbe

Advances in three-dimensional reconstruction of the experimental spinal cord injury

B.S. Duerstock, C.L. Bajaj, V. Pascucci, D. Schikore, K.N. Lin, R.B. Borgens
2000 Computerized Medical Imaging and Graphics  
We also show for the first time, that determination of the volume and surface area of pathological features is possible using the reconstructed 3D images themselves.  ...  Three-dimensional (3D) computer reconstruction is an ideal tool for evaluating the centralized pathology of mammalian spinal cord injury (SCI) where multiple anatomical features are embedded within each  ...  Acknowledgements We appreciate the technical aid of Debra Bohnert and Loren Moriarty during the conduct of these experiments and thank Carie Brackenbury and Jacqueline Libhart for aid in manuscript preparation  ... 
doi:10.1016/s0895-6111(00)00034-3 pmid:11008186 fatcat:txgxl2iddvb4xpotrp6szaaae4

Graph-Based Volumetric Data Segmentation on a Hexagonal-Prismatic Lattice

Mihai Popescu, Razvan Tanasie
2012 Conference on Computer Science and Information Systems  
In this paper we present a graph-based volumetric data segmentation method based on a 3D hexagonal prismatic lattice.  ...  One of the main advantages are high isoperimetric quotient, near equidistant neighbours (ability to represent curves better, resulting in a better segmentation) and high connectivity.  ...  This is the 3D extension of the "Brick Wall" lattice first proposed by Fitz and Green [3] for hexagonal image latices.  ... 
dblp:conf/fedcsis/PopescuT12 fatcat:4rmh7wzbznct5kiwmrofo3c3ba

3D Deep Learning on Medical Images: A Review

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 Sensors  
In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images.  ...  We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization  ...  [34] presented a modified version of the original U-Net i.e., 3D U-Net for volumetric segmentation from sparse notation. 3D Medical Imaging Pre-Processing Preprocessing of the image dataset before  ... 
doi:10.3390/s20185097 pmid:32906819 pmcid:PMC7570704 fatcat:top2ambpizdzdpsqamz2xm643u

as -PSOCT: Volumetric microscopic imaging of human brain architecture and connectivity

Hui Wang, Caroline Magnain, Ruopeng Wang, Jay Dubb, Ani Varjabedian, Lee S. Tirrell, Allison Stevens, Jean C. Augustinack, Ender Konukoglu, Iman Aganj, Matthew P. Frosch, Jeremy D. Schmahmann (+2 others)
2018 NeuroImage  
regions, all based on volumetric reconstructions. as-PSOCT provides a viable tool for studying quantitative cytoarchitecture and myeloarchitecture and mapping connectivity with microscopic resolution  ...  By using intrinsic optical properties of back-scattering and birefringence, PSOCT reliably images cytoarchitecture, myeloarchitecture and fiber orientations.  ...  In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies.  ... 
doi:10.1016/j.neuroimage.2017.10.012 pmid:29017866 pmcid:PMC5732037 fatcat:tr6imkudzfbbrdkn5pfn2ugdtu

Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks

Martin Pfister, Kornelia Schützenberger, Ulrike Pfeiffenberger, Alina Messner, Zhe Chen, Valentin Aranha dos Santos, Stefan Puchner, Gerhard Garhöfer, Leopold Schmetterer, Martin Gröschl, René M. Werkmeister
2019 Biomedical Optics Express  
Volumetric image data was acquired using a custom-built OCT prototype that employs an akinetic swept laser at ~1310 nm with a bandwidth of 87 nm, providing an axial resolution of ~6.5 μm in tissue.  ...  We present a system for automatic determination of the intradermal volume of hydrogels based on optical coherence tomography (OCT) and deep learning.  ...  Acknowledgments Different formulations of dermal fillers were kindly provided by Croma-Pharma GmbH. We thank Christine Hohenadl for critically reviewing the manuscript.  ... 
doi:10.1364/boe.10.001315 pmid:30891348 pmcid:PMC6420291 fatcat:msia2shd25blbhnkq3niywh3eu
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