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Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation [article]

Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
2020 arXiv   pre-print
In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images.  ...  Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture  ...  We argue that a unified attention approach that considers both the spatial-channel dependency and the efficiency of computation is of great practical value for modern 3D Medical image tasks.  ... 
arXiv:2009.05576v1 fatcat:x2yictbsobgy5gd67nqlqioqde

Technical note: Accelerated nonrigid motion-compensated isotropic 3D coronary MR angiography

Teresa Correia, Gastão Cruz, Torben Schneider, René M. Botnar, Claudia Prieto
2017 Medical Physics (Lancaster)  
Conclusion The feasibility of a highly efficient motion-compensated reconstruction framework for accelerated 3D CMRA has been demonstrated in healthy subjects.  ...  However, a slight reduced visibility of the distal segment of the RCA can be observed for subjects 6 and 7 in the ACOMoCo reconstructions.  ... 
doi:10.1002/mp.12663 pmid:29131353 pmcid:PMC5814733 fatcat:7zysugafizf2ne7dfuvwt7defu

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI [article]

Haoran Dou, Davood Karimi, Caitlin K. Rollins, Cynthia M. Ortinau, Lana Vasung, Clemente Velasco-Annis, Abdelhakim Ouaalam, Xin Yang, Dong Ni, Ali Gholipour
2021 arXiv   pre-print
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding.  ...  Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique.  ...  Our comparisons also showed that our proposed model was superior to prior attentive models for medical image segmentation [39] , [40] .  ... 
arXiv:2004.12847v3 fatcat:r26i6yn4tnhhrn66ld2kfnrp6m

Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks [article]

Artem Razumov, Oleg Y. Rogov, Dmitry V. Dylov
2021 arXiv   pre-print
We find the optimal undersampling patterns in k-space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose  ...  We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation  ...  For the segmentation task, we use U-Net [53] as the baseline model, and U-Net with attention [54] as the SOTA model for ACDC dataset and 3D U-Net [55] as the acknowledged benchmark for BraTS dataset  ... 
arXiv:2108.04914v1 fatcat:muqf3kexzjetbo7og6yminj3ei

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning [article]

Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi
2020 arXiv   pre-print
In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation  ...  Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction  ...  To date, almost all of them are 2D medical images. Non-medical 3D dataset.  ... 
arXiv:2003.02920v2 fatcat:ecsico6livgwpbmlqgjyuijvwe

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images [article]

Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu
2022 arXiv   pre-print
In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation.  ...  Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying  ...  However, UNETR has shown better performance in terms of both accuracy and efficiency in different medical image segmentation tasks [16] .  ... 
arXiv:2201.01266v1 fatcat:kst2ddakorayfl4zfpimuacspy

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data [article]

Meng Ye, Qiaoying Huang, Dong Yang, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris Metaxas
2020 arXiv   pre-print
With segmentation results, a 3D surface mesh and a corresponding point cloud of the segmented cardiac volume can be reconstructed for further analyses.  ...  and Hausdorff distance.The proposed joint learning framework of our PC-U net is beneficial for automatic cardiac image analysis tasks because it can obtain simultaneously the 3D shape and segmentation  ...  Each vertex v has a 3D coordinate (x, y, z). The set of the vertices V forms a point cloud. Point cloud-based shape representations have received increasing attention in medical image analysis.  ... 
arXiv:2008.08194v1 fatcat:x3mqxln7nnexjl76toktt34uni

A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark [article]

Yunhe Gao, Mu Zhou, Di Liu, Zhennan Yan, Shaoting Zhang, Dimitris N. Metaxas
2022 arXiv   pre-print
To tackle these challenges, we present UTNetV2 as a data-scalable Transformer towards generalizable medical image segmentation.  ...  However, existing vision Transformers struggle to learn with limited medical data and are unable to generalize on diverse medical image tasks.  ...  The contribution of this paper lies in four folds: • We propose a hybrid hierarchical architecture for 2D and 3D medical image segmentation.  ... 
arXiv:2203.00131v3 fatcat:dmuh4yga4rahzjjdy4ttg7eei4

Deep Negative Volume Segmentation [article]

Kristina Belikova, Oleg Rogov, Aleksandr Rybakov, Maxim V. Maslov, Dmitry V. Dylov
2020 arXiv   pre-print
Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation, a 3D volume construction by inflation of the reconstructed bone head in all directions along the normal vector to its  ...  To address the challenge, we invent a new angle to the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object - the so-called negative volume segmentation  ...  Medical image segmentation.  ... 
arXiv:2006.12430v1 fatcat:rddi7cpg7natde4kjuomzkahli

BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation

Zhuofu Deng, Binbin Wang, Zhiliang Zhu
2020 Mathematical Problems in Engineering  
However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in  ...  At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks.  ...  are more efficient to deal with 3D medical image segmentation maintaining acceptable accuracy.  ... 
doi:10.1155/2020/5689301 fatcat:eyqyyntn4vdphgf26cgxkuz2e4

Design a model of Image Restoration using AI in Digital Image Processing

Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
2021 Turkish Journal of Computer and Mathematics Education  
Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion.  ...  Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search.  ...  For multimode medical image segmentation, the fusion strategy plays an important role in achieving accurate segmentation results.  ... 
doi:10.17762/turcomat.v12i5.1497 fatcat:5uze4pz3ybblfagx2nxvvjcxfq

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Reza Kalantar, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, Dow-Mu Koh
2021 Diagnostics  
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing  ...  DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology.  ...  and (c) U-Net architecture with skip connections between encoder and decoder in the network for more efficient feature extraction/reconstruction than FCN.  ... 
doi:10.3390/diagnostics11111964 pmid:34829310 pmcid:PMC8625809 fatcat:alr36jtq6fgeddnluclp5neb2i

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhike Zhang, Shuixin Zhang, Hongyu Feng
2021 Journal of Healthcare Engineering  
The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.  ...  Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve  ...  To provide effective data support for the subsequent medical diagnosis, we finally selected the annular region of interest from 4-fold optic disc radius to 10-fold optic disc radius or 12-fold optic disc  ... 
doi:10.1155/2021/5573381 doaj:badcf56707244c63bd8fe0b5fcf031a3 fatcat:f45wcy3uq5bg3dkavg7xsvme74

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation with or without Intravenous Contrast

Anirudh Chandrashekar, Ashok Handa, Natesh Shivakumar, Pierfrancesco Lapolla, Raman Uberoi, Vicente Grau, Regent Lee
2020 Annals of Surgery  
Implementation of this network within the aortic segmentation pipeline for both contrast and non-contrast CT images has allowed for accurate and efficient extraction of the morphological and pathological  ...  In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood  ...  Attention Gating to strengthen U-Net Performance An attention-gated 3D U-Net was evaluated for the segmentation of the aneurysmal aorta.  ... 
doi:10.1097/sla.0000000000004595 pmid:33234786 fatcat:o5s6hllvibhudeimtftzykuiiy

A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms [article]

Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi
2021 arXiv   pre-print
The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning.  ...  A user first generates a surface model by manually specifying multiple thresholds for time-of-flight magnetic resonance angiography images.  ...  The main contributions of this study are as follows: (1) We propose a complete pipeline using point-based 3D deep neural networks for aneurysm segmentation from entire medical images.  ... 
arXiv:2006.16161v2 fatcat:dt4tazh26vag5mnj3yzxglkreq
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