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An Improved U-Net for Diabetic Retinopathy Segmentation 25th

An Improved U-Net for Diabetic Retinopathy Segmentation

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25th international conference on medical image computing and computer-assisted intervention , MICCAI 2022

作者： Chen, Xin Chen, Yanbin Lin, Chaonan Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031336577

Diabetic retinopathy (DR) is a common diabetic complication that can lead to blindness in severe cases. Ultra-wide (swept source) optical coherence tomography angiography(UW-OCTA) imaging can help ophthalmologists in the diagnosis of DR. Automatic and accurate segmentation of the lesion area is essential in the diagnosis of DR. However, there still remain several challenges for accurately segmenting lesion areas from UW-OCTA: the various lesion locations, diverse morphology and blurred contrast. To solve these problems, in this paper, we propose a novel framework to segment neovascularization(NV), nonperfusion areas(NA) and intraretinal microvascular abnormalities(IMA), which consists of two parts: 1) We respectively input the images of three lesions into three different channels to achieve three different lesions segmentation at the same time;2) We improve the traditional 2D U-Net by adding the residual module and dilated convolution. We evaluate the proposed method on the Diabetic Retinopathy Analysis Challenge (DRAC) in MICCAI2022. the mean Dice and mean IoU obtained by the method in the test cases are 0.4757 and 0.3538, respectively. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image segmentation

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1st international Workshops on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016 and 1st international Workshops on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016 Held in Conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

1st International Workshops on Reconstruction and Analysis o...

引用

1st international Workshops on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016 and 1st international Workshops on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016 Held in Conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

ISBN: (纸本)9783319522791

the proceedings contain 17 papers. the special focus in this conference is on Reconstruction and Analysis of Moving Body Organs. the topics include: Point-spread-function-aware slice-to-volume registration;motion correction using subpixel image registration;robust reconstruction of accelerated perfusion MRI using local and nonlocal constraints;graph-based 3D-ultrasound reconstruction of the liver in the presence of respiratory motion;motion estimated-compensated reconstruction with preserved-features in free-breathing cardiac MRI;recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation;dense volumetric segmentation for cardiovascular MRI volumes.;a GPU based diffusion method for whole-heart and great vessel segmentation;strengths and pitfalls of whole-heart atlas-based segmentation in congenital heart disease patients;automatic heart and vessel segmentation using random forests and a local phase guided level set method;total variation random forest and fully automatic MRI segmentation in congenital heart diseases.

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TransDUNet:image segmentation of artificial joint based on TransUNet

TransDUNet:Image segmentation of artificial joint based on T...

引用

6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Jingkun Tang College of Computer Science Sichuan University Chengdu China

Traditional convolution has limited ability to capture long-range dependencies of images, which is crucial for semantic segmentation in medical images, where spatial relationships between different anatomical structures are compound and non-local. the long-range dependencies in medical images can be captured by multi-head self-attention mechanisms, which is essential for complex and non-local spatial relationships between different anatomical structures. the receptive field of standard up-sampling convolutions is limited, which can affect the accuracy of segmentation predictions. Dilated convolutions can expand the receptive field and capture multi-scale contextual information. this paper proposes a new deep learning architecture called TransDUNet for semantic segmentation of artificial joint implants in medical images. Based on TransUNet, We replace the traditional convolution in the encoder up-sampling module of TransUNet with the module consisting of multiple dilated convolutions with different dilation rates to Incorporating multi-scale information, Dense skip connections are utilized to enhance feature reuse and gradient flow, thereby improving the performance and generalization ability of the model. the proposed algorithm’s segmentation performance outperforms recently developed networks, according to experiments, which demonstrate that its mDSC and mIoU are 94.1% and 88.68%, respectively.

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7th international Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

7th International Workshop on Statistical Atlases and Comput...

引用

7th international Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

ISBN: (纸本)9783319527178

the proceedings contain 24 papers. the special focus in this conference is on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges. the topics include: image-based real-time motion gating of 3D cardiac ultrasound images;novel framework to integrate real-time MR-guided EP data with T1 mapping-based computational heart models;left atrial appendage segmentation based on ranking 2-D segmentation proposals;correction of slice misalignment in multi-breath-hold cardiac MRI scans;segmentation and registration coupling from short-axis cine MRI;application to infarct diagnosis;learning optimal spatial scales for cardiac strain analysis using a motion atlas;3D reconstruction of coronary veins from a single X-ray fluoroscopic image and pre-operative MR;cartan frame analysis of hearts with infarcts;from CMR image to patient-specific simulation and population-based analysis;segmentation and tracking of myocardial boundaries using dynamic programming;registration with adjacent anatomical structures for cardiac resynchronization therapy guidance;4D automatic centre detection of the right and left ventricles from cine short-axis MRI;left-ventricle basal region constrained parametric mapping to unitary domain;myocardial scar quantification using SLIC supervoxels - parcellation based on tissue characteristic strains;segmentation challenge on the quantification of left atrial wall thickness;left atrial wall segmentation using clinically correlated metrics and automatic left atrial wall segmentation from contrast-enhanced CT angiography images.

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Semi-supervised Meta-learning with Disentanglement for Domain-Generalised medical image Segmentation 24th

Semi-supervised Meta-learning with Disentanglement for Domai...

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international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Liu, Xiao thermos, Spyridon O'Neil, Alison Tsaftaris, Sotirios A. Univ Edinburgh Sch Engn Edinburgh EH9 3FB Midlothian Scotland Alan Turing Inst London England Canon Med Res Europe Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9783030871963;9783030871956

Generalising deep models to new data from new centres (termed here domains) remains a challenge. this is largely attributed to shifts in data statistics (domain shifts) between source and unseen domains. Recently, gradient-based meta-learning approaches where the training data are split into meta-train and meta-test sets to simulate and handle the domain shifts during training have shown improved generalisation performance. However, the current fully supervised meta-learning approaches are not scalable for medical image segmentation, where large effort is required to create pixel-wise annotations. Meanwhile, in a low data regime, the simulated domain shifts may not approximate the true domain shifts well across source and unseen domains. To address this problem, we propose a novel semi-supervised meta-learning framework with disentanglement. We explicitly model the representations related to domain shifts. Disentangling the representations and combining them to reconstruct the input image allows unlabeled data to be used to better approximate the true domain shifts for meta-learning. Hence, the model can achieve better generalisation performance, especially when there is a limited amount of labeled data. Experiments show that the proposed method is robust on different segmentation tasks and achieves state-of-the-art generalisation performance on two public benchmarks. Code is publicly available at: https://***/vios-s/DGNet.

关键词： Domain generalisation Disentanglement medical image segmentation

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5th international Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd international Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st international Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

引用

5th international Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd international Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st international Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319675633

the proceedings contain 18 papers. the special focus in this conference is on Computational Methods for Molecular Imaging. the topics include: Individual analysis of molecular brain imaging data through automatic identification of abnormality patterns;3D alpha matting based co-segmentation of tumors on PET-CT images;dynamic respiratory motion estimation using patch-based kernel-PCA priors for lung cancer radiotherapy;mass transportation for deformable image registration with application to lung ct;motion-robust spatially constrained parameter estimation in renal diffusion weighted MRI by 3D motion tracking and correction of sequential slices;semi-automatic cardiac and respiratory gated MRI for cardiac assessment during exercise;freehand ultrasound image simulation with spatially-conditioned generative adversarial networks;context-sensitive super-resolution for fast fetal magnetic resonance imaging;reconstruction of 3D cardiac MR images from 2D slices using directional total variation;automated ventricular system segmentation in CT images of deformed brains due to ischemic and subarachnoid hemorrhagic stroke;towards automatic collateral circulation score evaluation in ischemic stroke using image decompositions and support vector machines;the effect of non-contrast ct slice thickness on thrombus density and perviousness assessment.

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Soft Tissue Tumor Detection Using U-Net

Soft Tissue Tumor Detection Using U-Net

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Ayush Kasliwal Jayant Shende Aryan Fuse Abhishek Borde Vidya Dhamdhere Computer Engineering Department G H Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Soft tissue tumor detection is vital for accurate diagnosis and treatment planning in clinical practice. In this manuscript, we present a fresh perspective for soft tissue tumor detection using the U-Net architecture, a powerful convolutional neural network (CNN) model widely employed in medical image segmentation tasks. Our method leverages the inherent capabilities of U-Net for precise delineation of soft tissue tumors from medical images. We augment the U-Net model with advanced techniques such as region growing and confidence-connected thresholding to enhance segmentation accuracy. through comprehensive experimentation on diverse datasets, we demonstrate the efficacy of our approach in accurately detecting soft tissue tumors across various modalities and imaging conditions. Comparative analysis with existing methods underscores the superiority of our proposed method in terms of both accuracy and computational efficiency. Moreover, we discuss the practical implications and potential applications of our approach in clinical settings, highlighting its utility for assisting medical professionals in accurate tumor detection and treatment planning. Overall, our proposed method represents a significant advancement in soft tissue tumor detection, offering a reliable and efficient solution for clinical diagnosis and management.

关键词： Training image segmentation Accuracy Biological tissues computer architecture Data models Planning medical diagnostic imaging Tumors Testing

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DA-VSR: Domain Adaptable Volumetric Super-Resolution for medical images 24th

DA-VSR: Domain Adaptable Volumetric Super-Resolution for Med...

引用

international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Peng, Cheng Zhou, S. Kevin Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Med Imaging Robot & Analyt Comp Lab Beijing Peoples R China Engn MIRACLE Grp Beijing Peoples R China Univ Sci & Technol China Sch Biomed Engn Suzhou 215123 Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou 215123 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China

ISBN: (纸本)9783030872311;9783030872304

medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc. However, applying deep-learning-based SR approaches for clinical applications often encounters issues of domain inconsistency, as the test data may be acquired by different machines or on different organs. In this work, we present a novel algorithm called domain adaptable volumetric super-resolution (DA-VSR) to better bridge the domain inconsistency gap. DA-VSR uses a unified feature extraction backbone and a series of network heads to improve image quality over different planes. Furthermore, DA-VSR leverages the in-plane and through-plane resolution differences on the test data to achieve a self-learned domain adaptation. As such, DA-VSR combines the advantages of a strong feature generator learned through supervised training and the ability to tune to the idiosyncrasies of the test volumes through unsupervised learning. through experiments, we demonstrate that DA-VSR significantly improves super-resolution quality across numerous datasets of different domains, thereby taking a further step toward real clinical applications.

关键词： image enhancement

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Two-Stage Semi-supervised nnU-Net Framework for Tooth Segmentation in CBCT images

Two-Stage Semi-supervised nnU-Net Framework for Tooth Segme...

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Ji, Changkai Liu, Yusheng He, Lanshan Jiang, Yuxian Huang, Chuanyi Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031889769

Automated tooth segmentation in Cone-Beam Computed Tomography (CBCT) images is crucial for various dental applications, including treatment planning and computer-assisted dental prosthesis design. the MICCAI STS 2024 Challenge Task 2 aims to enhance automated tooth segmentation by providing a dataset comprising both labeled and unlabeled CBCT images. this paper addresses the challenge of limited labeled data by reformulating the problem as a semi-supervised learning task. We propose a two-stage deep learning model based on nnU-Net. Our approach initially employs a low-resolution nnU-Net for quadrant segmentation, followed by a full-resolution nnU-Net for fine tooth segmentation within each quadrant. To efficiently utilize unlabeled data, we implement a selective stability-based retraining strategy to generate reliable pseudo-labels. Our method is quantitatively evaluated on the STS 2024 validation set, achieving good performance across various metrics (Dice_instance = 90.74%, Dice_image = 97.70%). the proposed approach achieved one of the highest rankings in the competition’s validation phase, demonstrating its efficacy in automatically segmenting teeth from CBCT images. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Deep Iterative 2D/3D Registration 24th

Deep Iterative 2D/3D Registration

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international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Jaganathan, Srikrishna Wang, Jian Borsdorf, Anja Shetty, Karthik Maier, Andreas FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Siemens Healthineers AG Forchheim Germany

ISBN: (纸本)9783030872021;9783030872014

Deep Learning-based 2D/3D registration methods are highly robust but often lack the necessary registration accuracy for clinical application. A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy. However, it also increases the runtime. In this work, we propose a novel Deep Learning driven 2D/3D registration framework that can be used end-to-end for iterative registration tasks without relying on any further refinement step. We accomplish this by learning the update step of the 2D/3D registration framework using Point-to-Plane Correspondences. the update step is learned using iterative residual refinement-based optical flow estimation, in combination with the Point-to-Plane correspondence solver embedded as a known operator. Our proposed method achieves an average runtime of around 8s, a mean re-projection distance error of 0.60 +/- 0.40 mm with a success ratio of 97% and a capture range of 60mm the combination of high registration accuracy, high robustness, and fast runtime makes our solution ideal for clinical applications.

关键词： Deep learning image fusion 2D/3D registration

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