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Feature preserving smoothing provides simple and effective data augmentation for medical image segmentation 23rd

Feature preserving smoothing provides simple and effective d...

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23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Sheikh, Rasha Schultz, thomas University of Bonn Bonn Germany

ISBN: (纸本)9783030597092

CNNs represent the current state of the art for image classification, as well as for image segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards texture, and that reducing it can increase the networks accuracy. We hypothesize that CNNs for medical image segmentation might suffer from a similar bias. We propose to reduce it by augmenting the training data with feature preserving smoothing, which reduces noise and high-frequency textural features, while preserving semantically meaningful boundaries. Experiments on multiple medical image segmentation tasks confirm that, especially when limited training data is available or a domain shift is involved, feature preserving smoothing can indeed serve as a simple and effective augmentation technique. © Springer Nature Switzerland AG 2020.

关键词： image classification

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One Network to Solve them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline 4th

One Network to Solve Them All: A Sequential Multi-task Joint...

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4th international Workshop on Machine Learning for medical Reconstruction (MLMIR) held as part of the e 24th medical image computing and computer assisted intervention (MICCAI)

作者： Wang, Zhiwen Xia, Wenjun Lu, Zexin Huang, Yongqiang Liu, Yan Chen, Hu Zhou, Jiliu Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Chengdu 610225 Peoples R China

ISBN: (纸本)9783030885526;9783030885519

Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notice that there are significant relevances among these tasks and this procedure artificially cuts off these potential connections, which may lead to losing clinically important information for the final diagnosis. To involve these potential relations for further performance improvement, a sequential multi-task joint learning network model is proposed to train a combined end-to-end pipeline in a differentiable way, aiming at exploring the mutual influence among those tasks simultaneously. Our design consists of three cascaded modules: 1) deep sampling pattern learning module optimizes the k-space sampling pattern with predetermined sampling rate;2) deep reconstruction module is dedicated to reconstructing MR images from the undersampled data using the learned sampling pattern;3) deep segmentation module encodes MR images reconstructed from the previous module to segment the interested tissues. the proposed model retrieves the latently interactive and cyclic relations among those tasks, from which each task will be mutually beneficial. the proposed framework is verified on MRB dataset, which achieves superior performance on other SOTA methods in terms of both reconstruction and segmentation. the code is available online: https://***/Deep-Imaging-Group/SemuNet.

关键词： Fast MRI Deep learning Sampling learning image reconstruction Segmentation

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5th international Workshop on Clinical image-Based Procedures, CLIP 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

5th International Workshop on Clinical Image-Based Procedure...

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5th international Workshop on Clinical image-Based Procedures, CLIP 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

ISBN: (纸本)9783319464718

the proceedings contain 10 papers. the special focus in this conference is on Clinical image-Based Procedures, medical image computing and computer-assisted intervention. the topics include: Detection of wrist fractures in X-Ray images, performance metrics for visual registration, instrument guidance, and image fusion;stable anatomical structure tracking for video-bronchoscopy navigation;uncertainty quantification of cochlear implant insertion from CT images;validation of an improved patient-specific mold design for registration of in-vivo MRI and Histology of the prostate;trajectory smoothing for guiding aortic valve delivery with transapical access;geodesic registration for cervical cancer radiotherapy;personalized optimal planning for the surgical correction of metopic craniosynostosis;towards a statistical shape-aware deformable contour model for cranial nerve identification and an automatic free fluid detection for Morrison’s-pouch.

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2nd international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2016 held in conjunction with 19th international conference on medical image computing and computer assisted intervention, MICCAI 2016

2nd International Workshop on Patch-Based Techniques in Medi...

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2nd international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2016 held in conjunction with 19th international conference on medical image computing and computer assisted intervention, MICCAI 2016

ISBN: (纸本)9783319471174

the proceedings contain 17 papers. the special focus in this conference is on medical image computing and computer assisted intervention. the topics include: Automatic segmentation of hippocampus for longitudinal infant Brain MR image sequence by spatial-temporal hypergraph learning;construction of neonatal diffusion atlases via spatio-angular consistency;identifying representative sub-volumes for interactive segmentation;robust and accurate appearance models based on joint dictionary learning;data from the osteoarthritis initiative;consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation;principle and application to Alzheimer’s disease;multi-atlas based segmentation of brainstem nuclei from MR images by deep hyper-graph learning;patch-based discrete registration of clinical brain images;application to Alzheimer’s disease classification;hyperintensity segmentation tool;supervoxel-based hierarchical Markov random field framework for multi-atlas segmentation and estimating lung respiratory motion using combined global and local statistical models.

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A feature-pooling and signature-pooling method for feature selection for quantitative image analysis: Application to a radiomics model for survival in Glioma 1

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1st international Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, held in conjunction with the 22nd international conference on medical image computing and computer-assisted intervention, MICCAI 2019

作者： Shi, Zhenwei Zhang, Chong Compter, Inge Verduin, Maikel Hoeben, Ann Eekers, Danielle Dekker, Andre Wee, Leonard GROW School for Oncology and Developmental Biology Maastricht University Medical Centre+ Maastricht Netherlands Department of Medical Oncology GROW School for Oncology and Developmental Biology Maastricht University Medical Centre+ Maastricht Netherlands

ISBN: (数字)9783030401245

ISBN: (纸本)9783030401238

We proposed a pooling-based radiomics feature selection method and showed how it would be applied to the clinical question of predicting one-year survival in 130 patients treated for glioma by radiotherapy. the method combines filter, wrapper and embedded selection in a comprehensive process to identify useful features and build them into a potentially predictive signature. the results showed that non-invasive CT radiomics were able to moderately predict overall survival and predict WHO tumour grade. this study reveals an associative inter-relationship between WHO tumour grade, CT-based radiomics and survival, that could be clinically relevant. © Springer Nature Switzerland AG 2020.

关键词： Feature Selection

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

5th International MICCAI Brainlesion Workshop, BrainLes 2019...

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030466428

the proceedings contain 75 papers. the special focus in this conference is on Brainlesion Workshop. the topics include: 3D automatic brain tumor segmentation using a multiscale input U-Net network;semi-supervised variational autoencoder for survival prediction;multi-modal U-Nets with boundary loss and pre-training for brain tumor segmentation;multidimensional and multiresolution ensemble networks for brain tumor segmentation;hybrid labels for brain tumor segmentation;Two stages CNN-Based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival;Detection and segmentation of brain tumors from MRI using U-Nets;Multimodal segmentation with MGF-Net and the focal tversky loss function;brain tumor segmentation using 3D convolutional neural network;multimodal brain image segmentation and analysis with neuromorphic attention-based learning;DDU-Nets: Distributed dense model for 3D MRI brain tumor segmentation;brain tumor segmentation based on 3D residual U-Net;Automatic segmentation of brain tumor from 3D MR images using SegNet, U-Net, and PSP-Net;3D deep residual encoder-decoder CNNS with squeeze-and-excitation for brain tumor segmentation;Overall survival prediction using conventional MRI features;a multi-path decoder network for brain tumor segmentation;the tumor Mix-Up in 3D unet for glioma segmentation;multi-branch learning framework with different receptive fields ensemble for brain tumor segmentation;domain knowledge based brain tumor segmentation and overall survival prediction;Encoder-decoder network for brain tumor segmentation on multi-sequence MRI;Improving brain tumor segmentation in multi-sequence MR images using cross-sequence MR image generation;deep convolutional neural networks for brain tumor segmentation: Boosting performance using deep transfer learning: Preliminary results;multimodal brain tumor segmentation with normal appearance autoencoder.

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Semi-supervised medical image classification with global latent mixing 23rd

Semi-supervised medical image classification with global lat...

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23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Gyawali, Prashnna Kumar Ghimire, Sandesh Bajracharya, Pradeep Li, Zhiyuan Wang, Linwei Rochester Institute of Technology Rochester United States

ISBN: (纸本)9783030597092

computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network. We evaluated the presented model on two distinct medical image data sets for semi-supervised classification of thoracic disease and skin lesion, demonstrating its improved performance over SSL with local perturbations and SSL with global mixing but at the input space only. Our code is available at https://***/Prasanna1991/LatentMixing. © Springer Nature Switzerland AG 2020.

关键词： image enhancement

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1st international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

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1st international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

ISBN: (纸本)9783319466293

the proceedings contain 17 papers. the special focus in this conference is on Simulation, Its Applications in Computational medical Imaging, Synthesis and Its Applications in Computational medical Imaging. the topics include: Software framework for realistic MRI simulations using the polyhedral Fourier transform;covering population variability;morphing of computation anatomical models;image-based PSF estimation for ultrasound training simulation;microstructure imaging sequence simulation toolbox;from image-based modeling to the modeling of imaging with the virtual population;GBM modeling with proliferation and migration phenotypes;a proposal of initialization for real cases;panoramic ultrasound reconstruction by seamless stitching of volumes;pseudo-healthy image synthesis for white matter lesion segmentation;registration of pathological images;whole image synthesis using a deep encoder-decoder network;MRI-TRUS image synthesis with application to image-guided prostate intervention and automatic generation of synthetic retinal fundus images;vascular network.

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Realistic adversarial data augmentation for mr image segmentation 23rd

Realistic adversarial data augmentation for mr image segment...

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23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Chen, Chen Qin, Chen Qiu, Huaqi Ouyang, Cheng Wang, Shuo Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel BioMedIA Group Department of Computing Imperial College London London United Kingdom Institute for Digital Communications University of Edinburgh Edinburgh United Kingdom CitAI Research Centre Department of Computer Science City University of London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom

ISBN: (纸本)9783030597092

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. the proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios. © Springer Nature Switzerland AG 2020.

关键词： image segmentation

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

5th International MICCAI Brainlesion Workshop, BrainLes 2019...

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030466398

the proceedings contain 75 papers. the special focus in this conference is on Brainlesion Workshop. the topics include: Saliency based deep neural network for automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI;deep learning for brain tumor segmentation in radiosurgery: prospective clinical evaluation;3d u-net based brain tumor segmentation and survival days prediction;aggregating multi-scale prediction based on 3d u-net in brain tumor segmentation;brain tumor synthetic segmentation in 3d multimodal mri scans;multi-step cascaded networks for brain tumor segmentation;tunet: end-to-end hierarchical brain tumor segmentation using cascaded networks;using separated inputs for multimodal brain tumor segmentation with 3d u-net-like architectures;two-step u-nets for brain tumor segmentation and random forest with radiomics for survival time prediction;Tbi lesion segmentation in head CT: Impact of preprocessing and data augmentation;bag of tricks for 3d mri brain tumor segmentation;multi-resolution 3d cnn for mri brain tumor segmentation and survival prediction;two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task;memory-efficient cascade 3d u-net for brain tumor segmentation;a baseline for predicting glioblastoma patient survival time with classical statistical models and primitive features ignoring image information;brain tumor segmentation and survival prediction using 3d attention unet;brain tumor segmentation using dense channels 2d u-net and multiple feature extraction network;Brain tumour segmentation on MRI images by voxel classification using neural networks, and patient survival prediction;oncohabitats glioma segmentation model;brain tumor segmentation with uncertainty estimation and overall survival prediction;multi-task learning for brain tumor segmentation.

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