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Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep Learning [article]

Andreas D. Lauritzen, Xenophon Papademetris, Sergei Turovets, John A. Onofrey
2019 arXiv   pre-print
We also present a novel synthesis method that combines multi-atlas registration as a prior to deep learning algorithms, in which we perform a weighted addition of synthetic CT images, derived from atlases  ...  Using a dataset of 30 paired MRI and CT image volumes, our results compare image synthesis using deep neural network regression, state-of-the-art adversarial deep learning, as well as atlas-based synthesis  ...  We propose a novel framework in which multi-atlas registration synthesis serves as a prior to a deep neural network (DNN).  ... 
arXiv:1906.04467v1 fatcat:we7bct5hnnatfcg7vpcwmgtjea

Eyeing the Human Brain's Segmentation Methods

Lilian Chirukawala et al., Lilian Chirukawala et al.,
2019 International Journal of Electrical and Electronics Engineering Research  
Image segmentation (IS) is often the first and most important step in medical image analysis.  ...  Conclusively, the study provides guidelines and directions for the appropriate segmentation approach(s) reliable and succinctly suite for MRI brain Images with good prospects.  ...  The alignment of a probabilistic atlas with the image to be segmented, will ensure the prior knowledge of the method.  ... 
doi:10.24247/ijeeerjun20195 fatcat:5cacelevgvci3n5wyiq6lr2jay

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation [article]

Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias
2019 arXiv   pre-print
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms.  ...  In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without  ...  In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies.  ... 
arXiv:1904.11319v2 fatcat:q3syhlr6vbav3mvobg2f2epmoy

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation [chapter]

Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias
2019 Lecture Notes in Computer Science  
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms.  ...  In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without  ...  In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies.  ... 
doi:10.1007/978-3-030-32248-9_40 pmid:32432231 pmcid:PMC7235150 fatcat:skx3u7lixjc4vdw42n3ljacpwa

Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks [chapter]

Eli Gibson, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve Bandula, Kurinchi Gurusamy, Brian R. Davidson, Stephen P. Pereira, Matthew J. Clarkson, Dean C. Barratt
2017 Lecture Notes in Computer Science  
We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior.  ...  Because robust interpatient registration of abdominal images is necessary for existing multi-atlas-and statistical-shape-modelbased segmentations, but remains challenging, there is a need for automated  ...  Spatial priors are more suited to medical images than natural images because medical images are commonly acquired in standard anatomically aligned views.  ... 
doi:10.1007/978-3-319-66182-7_83 fatcat:ytcivrczojav3gwakrfxz7rovm

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  Atlas-based segmentation [98] , [99] with single-or multiple-atlas has been widely used in medical image segmentation to exploit prior knowledge from previously labeled training images.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  [98] , [99] with single-or multiple-atlas has been widely used in medical image segmentation to exploit prior knowledge from previously labeled training images.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network [article]

Zhongliu Xie, Duncan Gillies
2018 arXiv   pre-print
In recent years, deep learning has revolutionalized computer vision with many practices outperforming prior art, in particular the convolutional neural network (CNN) studies on image classification.  ...  Deep CNN has also started being applied to medical image segmentation lately, but generally involves long training and demanding memory requirements, achieving limited success.  ...  Typically, the atlases are non-rigidly registered with a target image, and labels are propagated to perform segmentation by multi-atlas label propagation (MALP) [1] .  ... 
arXiv:1807.05482v1 fatcat:een77vg6arcvdksgjx7daamntu

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation [article]

Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray, Mohammad Saeed Rad, Jean-Philippe Thiran
2021 arXiv   pre-print
In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data.  ...  Instead, it can generate diversified volumetric image-segmentation pairs from a prior distribution given a single or multi-site dataset.  ...  using a single atlas or multi-atlas tackled weakly-supervised medical image segmentation.  ... 
arXiv:2110.02117v1 fatcat:ampolwvdi5hkrcz4goo5lcjlh4

An atlas-based deep brain structure segmentation method: from coarse positioning to fine shaping

Yishan Luo, Albert C.S. Chung
2011 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Segmentation of deep brain structures is a challenging task for MRI images due to blurry structure boundaries, small object size and irregular shapes.  ...  After positioning the structures, the segmentation result is further fine tuned by a non-rigid registration procedure between the atlas image and the target image using the histogram of the gradient magnitudes  ...  Based on these observations, our method is proposed with the following distinct features. (1) The deep brain structures are segmented in sequence, with a prior spatial dependency tree to constrain their  ... 
doi:10.1109/icassp.2011.5946596 dblp:conf/icassp/LuoC11 fatcat:3niilodjkjdqbi3c4zofkhen4a

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation [article]

Cong Xie, Hualuo Liu, Shilei Cao, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng
2022 arXiv   pre-print
Semantic segmentation is important in medical image analysis.  ...  Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject similarity, many deep learning (DL) models have been recently proposed to exploit such  ...  Second, intersubject similarity, which provided abundant resources for anatomical priors in classical medical image segmentation methods such as atlas-based segmentation [2] , is not fullyexploited.  ... 
arXiv:2204.11090v1 fatcat:gnhmxagre5a4tify2wt2qiyipe

Hybrid Atlas Building with Deep Registration Priors [article]

Nian Wu, Jian Wang, Miaomiao Zhang, Guixu Zhang, Yaxin Peng, Chaomin Shen
2022 arXiv   pre-print
In this paper, we introduce a novel hybrid atlas building algorithm that fast estimates atlas from large-scale image datasets with much reduced computational cost.  ...  In contrast to previous approaches that iteratively perform registration tasks between an estimated atlas and individual images, we propose to use learned priors of registration from pre-trained neural  ...  INTRODUCTION Image atlas (also known as mean template) has critical values in medical applications, as it provides an unbiased coordinate system for template-based image segmentation [1, 2], statistical  ... 
arXiv:2112.06406v3 fatcat:jmmgzcalxrbgbhhh7auru7f6ou

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Yanrong Guo, Yaozong Gao, Dinggang Shen
2016 IEEE Transactions on Medical Imaging  
To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching.  ...  Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation.  ...  The images are acquired with 1.5T magnetic field strength from different patients under different MR image scanners (34 images from Philips Medical Systems and 32 images from GE Medical Systems).  ... 
doi:10.1109/tmi.2015.2508280 pmid:26685226 pmcid:PMC5002995 fatcat:l7yvxkqngrd35p55ejcv2e55r4

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Sami Bourouis, Roobaea Alroobaea, Saeed Rubaiee, Anas Ahmed
2020 Information  
Accurate medical images analysis plays a vital role for several clinical applications.  ...  The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis.  ...  In the medical image analysis, atlas-guided methods have raised much interest since they exploit prior knowledge to achieve a precise objective (i.e., image segmentation and image registration).  ... 
doi:10.3390/info11030155 fatcat:ct45hdh4ovdulka6eirvwfgl3i

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

Kaisar Kushibar, Sergi Valverde, Sandra González-Villà, Jose Bernal, Mariano Cabezas, Arnau Oliver, Xavier Lladó
2018 Medical Image Analysis  
We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different available state-of-the-art methods and other recently proposed deep  ...  On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning  ...  The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN-X PASCAL GPU used in this research.  ... 
doi:10.1016/j.media.2018.06.006 pmid:29935442 fatcat:mdj2o4zrgjbu5g5g4fnq6r436i
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