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Medical Image Segmentation on MRI Images with Missing Modalities: A Review
[article]
2022
arXiv
pre-print
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Furthermore, the most commonly used MRI datasets are highlighted and described. ...
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. ...
, the Longitudinal multiple sclerosis lesion dataset [22] and the BraTS dataset [63] . ...
arXiv:2203.06217v1
fatcat:wbfhesrpajdy3pfqbnut6il32e
ResViT: Residual vision transformers for multi-modal medical image synthesis
[article]
2022
arXiv
pre-print
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Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. ...
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. ...
The bottleneck contains a cascade of residual CNN blocks. pix2pix A convolutional GAN model with U-Net backbone was considered [43] . pix2pix has a CNN-based generator with an encoder-decoder structure ...
arXiv:2106.16031v3
fatcat:2tsit33c2nhbfo7ejgg7cwxbze
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
[chapter]
2018
Lecture Notes in Computer Science
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Accurate synthesis of a full 3D MR image containing tumours from available MRI (e.g. to replace an image that is currently unavailable or corrupted) would provide a clinician as well as downstream inference ...
The hypothesis is that this would focus the network to perform accurate synthesis in the area of the tumour. ...
We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research. ...
doi:10.1007/978-3-030-00536-8_13
fatcat:onwgbovhlja6jpr35hop2nahuy
A Review of Deep-Learning-Based Medical Image Segmentation Methods
2021
Sustainability
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Now it has become an important research direction in the field of computer vision. ...
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. ...
[90] proposed an improved U-Net named LU-Net, in order to solve the problem of U-Net's low accuracy in cardiac ventricular segmentation. ...
doi:10.3390/su13031224
fatcat:pn2qbyv53zbuhhiuem2pc4dg3u
Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging
2017
Hanyang Medical Reviews
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use, distribution, and reproduction in any medium, provided the original work is properly cited. ...
Specifically, analysis of advanced modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) has benefited most from the data-driven nature of deep learning. ...
They could accurately segment the lesion by fine tuning a U-net shaped encoder-decoder network based on an encoder pretrained by a stacked restricted Boltzmann machine. ...
doi:10.7599/hmr.2017.37.2.61
fatcat:f4dl4szy35bhfilas3kyblzgui
Deep Semantic Segmentation of Natural and Medical Images: A Review
[article]
2020
arXiv
pre-print
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In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. ...
In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, ...
The main models being used for image segmentation mostly follow encoder-decoder architectures as U-Net. ...
arXiv:1910.07655v3
fatcat:uxrrmb3jofcsvnkfkuhfwi62yq
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
[article]
2020
arXiv
pre-print
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The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. ...
In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. ...
Zhang et al. (2017b) propose a semi-supervised learning framework according to an adversarial game between a segmentation network (U-Net) and an evaluation network (encoder). ...
arXiv:1908.10454v2
fatcat:mjvfbhx75bdkbheysq3r7wmhdi
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation
[article]
2021
arXiv
pre-print
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In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation. ...
Driven by the breakthroughs in computer vision, deep learning became the de facto standard in the domain of medical imaging. ...
sclerosis lesion segmentation in MRI. ...
arXiv:2103.05529v1
fatcat:iqu5ix5tgre6pnokdmoejywh74
Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images
2021
Diagnostics
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Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. ...
The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations ...
of the proximal femur in 3D MRI [18, 223] diagnosis-segmentation of multiple sclerosis [224] Medical Datasets Images
Methods of Incorporating
Medical images MRI data, CT angiography,
3DSeg ...
doi:10.3390/diagnostics11081373
fatcat:6p7usnvnxnewtivzeth745s3ga
U-Net and its variants for medical image segmentation: theory and applications
[article]
2020
arXiv
pre-print
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The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. ...
U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. ...
The attention unit is useful in encoder-decoder models such as the U-net since it can provide localized classification information as opposed to global classification. ...
arXiv:2011.01118v1
fatcat:u2blyrazp5hlhnvulidcvbtu64
Recent advances and clinical applications of deep learning in medical image analysis
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.13381v1 fatcat:yxa6asxvcra3bixp4habjdbcbaOther Versions
In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. ...
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging ...
For example, in the task of multiple sclerosis lesion detection where uncertainties mostly result from small lesions and lesion boundaries, Nair et al. (2020) explored using uncertainty estimates to improve ...
arXiv:2105.13381v2
fatcat:2k342a6rhjaavpoa2qoqxhg5rq
U-net and its variants for medical image segmentation: A review of theory and applications
2021
IEEE Access
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The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. ...
Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. ...
The attention unit is useful in encoder-decoder models such as the U-net since it can provide localized classification information as opposed to global classification. ...
doi:10.1109/access.2021.3086020
fatcat:b6cd45zsojfwhoer3sw5euei5e
Diffusion Models for Implicit Image Segmentation Ensembles
[article]
2021
arXiv
pre-print
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To generate an image specific segmentation, we train the model on the ground truth segmentation, and use the image as a prior during training and in every step during the sampling process. ...
This property allows us to compute pixel-wise uncertainty maps of the segmentation, and allows an implicit ensemble of segmentations that increases the segmentation performance. ...
A dense u-net architecture for multiple sclerosis lesion segmentation. In
TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pages 662–667. IEEE,
2019. ...
arXiv:2112.03145v2
fatcat:qulcdb2abvhdved2tvh2s56zqa
Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images
2022
IEEE Journal of Translational Engineering in Health and Medicine
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Objective: Multiple Sclerosis (MS) is an autoimmune and demyelinating disease that leads to lesions in the central nervous system. ...
This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI). ...
[31] proposed a dual encoder residual U-Net architecture to reduce the risk of losing local structure and necessary details. ...
doi:10.1109/jtehm.2022.3172025
pmid:35711337
pmcid:PMC9191687
fatcat:eke3flvnvnge3lw6qq3563vjke
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods
[article]
2021
arXiv
pre-print
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Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. ...
In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models ...
[134] used spatial and channel-wise attention in the decoder of the proposed U-Net architecture inorder to visualize features learnt at every resolution. ...
arXiv:2111.02398v1
fatcat:glrfdkbcqrbqto2nrl7dnlg3gq
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