A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep Learning
[article]
2019
arXiv
pre-print
Preserved Fulltext
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
2019
International Journal of Electrical and Electronics Engineering Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1904.11319v1 fatcat:mfdzmgpoejddplk2u6wn6gnvye
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]
2019
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2017
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
2021
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
unpublished
doi:10.1109/isbi48211.2021.9433936 fatcat:wn57i2moqrdindkxplw7v5bwea
doi:10.1109/isbi48211.2021.9433936 fatcat:wn57i2moqrdindkxplw7v5bwea
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2016
IEEE Transactions on Medical Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2020
Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2018
Medical Image Analysis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
« Previous
Showing results 1 — 15 out of 9,026 results