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Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation [article]

Jingkun Chen, Hongwei Li, Jianguo Zhang, Bjoern Menze
2019 arXiv   pre-print
We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning.  ...  To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline.  ...  Conclusions We propose an automated method for heart segmentation based on multi-modality MRI images, which is trained in an adversarial manner.  ... 
arXiv:1908.09298v2 fatcat:5plwchfkuzbxxpcavdunet7trq

Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation [article]

Fuping Wu, Xiahai Zhuang
2021 arXiv   pre-print
We validated the proposed domain adaptation method using two cardiac segmentation tasks, i.e., the cross-modality (CT and MR) whole heart segmentation and the cross-sequence cardiac MR segmentation.  ...  Most of the reported works mapped images of both the source and target domains into a common latent feature space, and then reduced their discrepancy either implicitly with adversarial training or explicitly  ...  [25] studied a multi-input multi-output fully convolutional neural network for MR synthesis.  ... 
arXiv:2106.08752v1 fatcat:yplkd6yvtnd7vhy6luozum3ctm

Multi-Modality Cardiac Image Analysis with Deep Learning [article]

Lei Li, Fuping Wu, Sihang Wang, Xiahai Zhuang
2021 arXiv   pre-print
Thirdly, we present three unsupervised domain adaptation techniques for cross-modality cardiac image segmentation.  ...  Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation.  ...  segmentation tasks, i.e., CT-MR cross modality cardiac segmentation, and C0-LGE multi-sequence CMR segmentation.  ... 
arXiv:2111.04736v1 fatcat:pdxoa7p23jhknc7rvtdydurqma

DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning

Cheng-Bin Jin, Hakil Kim, Mingjie Liu, In Ho Han, Jae Il Lee, Jung Hwan Lee, Seongsu Joo, Eunsik Park, Young Saem Ahn, Xuenan Cui
2019 Applied Sciences  
In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC 2 Anet) with semi-supervised  ...  More importantly, MRI is contraindicated for some patients with claustrophobia or cardiac pacemakers due to the possibility of injury.  ...  In response to this, we propose a synthesis method based on convolutional neural networks (CNNs) [1] with adversarial training [3] to produce a lumbar spine MR image from a CT scan.  ... 
doi:10.3390/app9122521 fatcat:a3syg3ey5nfw3gf3kx4qaicatq

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing.  ...  We conclude with a discussion and presentation of promising future directions.  ...  [60] , who successfully implemented the idea of combining motion and segmentation on 2D cardiac MR sequences by developing a dual Siamese style recurrent spatial transformer network and fully convolutional  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

Medical Image Segmentation Using Deep Learning: A Survey [article]

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi
2021 arXiv   pre-print
For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately.  ...  Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field.  ...  [117] also adopted a similar method using segmentation labels of MR images to achieve the task of cardiac CT segmentation.  ... 
arXiv:2009.13120v3 fatcat:ntgbqwkz55axrjum72elbm6rry

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets.  ...  This paper presents a review of deep learning (DL) based medical image registration methods.  ...  They performed on MR brain images for unimodal registration and on pelvic CT-MR for multi-modal registration. They have showed that the performance increased with the adversarial loss.  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4

Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation [article]

Nicolo Savioli, Antonio de Marvao, Wenjia Bai, Shuo Wang, Stuart A. Cook, Calvin W.L. Chin, Daniel Rueckert, Declan P. O'Regan
2021 arXiv   pre-print
and segmentations, while an unsupervised variational adversarial mixture autoencoder (V-AMA) is used for continuous domain adaptation.  ...  Here we propose a semi-supervised multi-task generative adversarial network (Gemini-GAN) that performs joint super-resolution of the images and their labels using a ground truth of high resolution 3D cines  ...  Acknowledgments The study was supported by Bayer AG; Medical Research Council (MC-A658-5QEB0); National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre; British Heart Foundation  ... 
arXiv:2107.07975v1 fatcat:b7hg44absrd3jiiulagg22cehi

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.  ...  data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations.  ...  The framework is trained and evaluated on the multi-sequence cardiac MR segmentation challenge (MS-CMRSeg 2019) dataset.  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

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

Jialin Peng, Ye Wang
2021 IEEE Access  
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  application of deep learning models in medical image segmentation.  ...  They proposed to condition a single convolutional network for multi-class segmentation with non-overlapping single-class datasets for training.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.  ...  This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018.  ...  [214] presented an Adversarial Image Registration (AIR) method for multi-modal image MR-TRUS registration [215] .  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi
2022 IET Image Processing  
For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately.  ...  Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field.  ...  [117] also adopted a similar method using segmentation labels of MR images to achieve the task of cardiac CT segmentation. Chartsias et al.  ... 
doi:10.1049/ipr2.12419 fatcat:zvgj3vdzqbfbzjoglgmtnn6ukq

Medical Image Generation using Generative Adversarial Networks [article]

Nripendra Kumar Singh, Khalid Raza
2020 arXiv   pre-print
The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical  ...  Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal  ...  detection of missing features from cardiac MR image Table 3 . 3 SummarizeCross Modality Synthesis: Cross modality synthesis, for example, creating CT equivalent image dependents on MR images is  ... 
arXiv:2005.10687v1 fatcat:5rg75wgww5d6vapjkfz4l2choi

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

Jialin Peng, Ye Wang
2021 arXiv   pre-print
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  application of deep learning models in medical image segmentation.  ...  They proposed to condition a single convolutional network for multi-class segmentation with non-overlapping single-class datasets for training.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Deep Semantic Segmentation of Natural and Medical Images: A Review [article]

Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
2020 arXiv   pre-print
In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics.  ...  sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.  ...  Encoder Decoder based Image Segmentation Attention based Image Segmentation Adversarial Training based Image Segmentation Sequenced Models The Recurrent Neural Network (RNN) was designed for handling  ... 
arXiv:1910.07655v3 fatcat:uxrrmb3jofcsvnkfkuhfwi62yq
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