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Brain MRI Tumor Segmentation with Adversarial Networks [article]

Edoardo Giacomello, Daniele Loiacono, Luca Mainardi
2020 arXiv   pre-print
In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks.  ...  Finally, we proposed to apply transfer learning across different contrast modalities to improve the performance of these single-modality models.  ...  [3] proposed an Adversarial Network with a Multi-Scale loss, called SegAN, achieving better performances compared to the state-of-the-art methods for brain tumor segmentation [8] , [9] . B.  ... 
arXiv:1910.02717v2 fatcat:bf4zwamknbeijgp56rr6f4fj5e

Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

Anirudh Choudhary, Li Tong, Yuanda Zhu, May D. Wang
2020 IMIA Yearbook of Medical Informatics  
Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks.  ...  Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.  ...  ., [28] leveraged a CycleGAN with semantic-aware adversarial loss to perform lung segmentation across different chest X-ray datasets.  ... 
doi:10.1055/s-0040-1702009 pmid:32823306 fatcat:gtlhoh6m3fh4hcumfzdlpdohr4

Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, Yizhou Yu
2020 Pattern Recognition  
To this end, this paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data.  ...  networks.  ...  [10] proposed a multi-modal convolutional network for brain tumor segmentation, where nested network structure was designed to explicitly leverage deep features within or across modalities.  ... 
doi:10.1016/j.patcog.2020.107562 fatcat:6lb4es3v3ngwdaenjo3x42e3he

Brain Tumor Segmentation on MRI with Missing Modalities [article]

Yan Shen, Mingchen Gao
2019 arXiv   pre-print
Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis.  ...  We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path.  ...  Conclusion We propose a brain tumor segmentation algorithm that is robust to missing modality.  ... 
arXiv:1904.07290v1 fatcat:xbipbgg5mna53ik47oh2qte4ga

A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI [article]

Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre, Umit Topaloglu, Richard Barcus, Michael D. Chan, Christina K. Cramer, Waldemar Debinski, Metin N. Gurcan, Glenn J. Lesser, Hui-Kuan Lin, Reginald F. Munden (+8 others)
2021 arXiv   pre-print
Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow for tumor segmentation, modality  ...  Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data.  ...  Guangxu Jin and Liang Liu with Wake Forest Baptist Comprehensive Cancer Center Bioinformatics Shared Resource for their inputs.  ... 
arXiv:2110.03588v4 fatcat:sizy3dm2d5hf5gtrncg3p4unhi

Learning Cross-Modality Representations from Multi-Modal Images

Gijs van Tulder, Marleen de Bruijne
2018 IEEE Transactions on Medical Imaging  
This paper presents experiments on two public datasets, with knee images from two MRI modalities, provided by the Osteoarthritis Initiative, and brain tumor segmentation on four MRI modalities from the  ...  We investigate a form of feature normalization, a learning objective that minimizes crossmodality differences, and modality dropout, in which the network is trained with varying subsets of modalities.  ...  We used supercomputer facilities sponsored by NWO Exact and Natural Sciences and used public data from the Osteoarthritis Initiative (OAI) and the Brain Tumor Segmentation Challenge (BRATS).  ... 
doi:10.1109/tmi.2018.2868977 pmid:30188817 fatcat:jyyfmnctgjdnfher66ii7itelq

Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training [article]

Harrison Nguyen, Simon Luo, Fabio Ramos
2019 arXiv   pre-print
Current state-of-the-art models for brain tissue segmentation and disease classification require multiple modalities for training and inference.  ...  Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used to investigate a wide  ...  BraTS utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.  ... 
arXiv:1912.04391v1 fatcat:x6iyb7stjreezjayzmli2klb3e

GANs for Medical Image Analysis [article]

Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay
2019 arXiv   pre-print
generative models.  ...  Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction  ...  In short, along with exciting results, GANs open up many possible research questions for the next few years.  ... 
arXiv:1809.06222v3 fatcat:gfsmlq3uhvd4xeisaqagythgeq

Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training [chapter]

Harrison Nguyen, Simon Luo, Fabio Ramos
2020 Lecture Notes in Computer Science  
Current state-of-the-art models for brain tissue segmentation and disease classification require multiple modalities for training and inference.  ...  Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used to investigate a wide  ...  BraTS utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.  ... 
doi:10.1007/978-3-030-47436-2_31 fatcat:wa3ang7pujhb3f23gtfkoze7xq

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection [article]

Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley
2019 arXiv   pre-print
We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart  ...  Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels.  ...  Results on more MRI modalities with brain tumor.  ... 
arXiv:1810.10850v2 fatcat:kz4ihpfirngxvm4vfs2qwl5kze

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh, Riccardo De Feo, Mithilesh Prakash, Robert Ciszek, Jussi Tohka
2021 Journal of Imaging  
The most frequent applications were dementia-related classification tasks and brain tumor segmentation.  ...  Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications.  ...  For instance, an ML model trained for brain extraction (skull-stripping) might be useful when training an ML model for tumor segmentation.  ... 
doi:10.3390/jimaging7040066 pmid:34460516 pmcid:PMC8321322 fatcat:qpqjwl4bybhsfd4vdsnt3vyyye

U-Net and its variants for medical image segmentation: theory and applications [article]

Nahian Siddique, Paheding Sidike, Colin Elkin, Vijay Devabhaktuni
2020 arXiv   pre-print
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.  ...  These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging.  ...  net Brain tumor [80] MR De-aliasing Adversarial net Brain tumor [248] , [249] MR Image registration Base U-net Brain tissue [84] MR Image registration Adversarial net Brain tissue [250] MR Image  ... 
arXiv:2011.01118v1 fatcat:u2blyrazp5hlhnvulidcvbtu64

Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Yue Zhao, Xiaoqiang Ren, Kun Hou, Wentao Li
2021 Symmetry  
Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis.  ...  In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is  ...  Discussion In this paper, we come up with a novel architecture for brain tumor segmentation from multi-modal 3D MRIs.  ... 
doi:10.3390/sym13020320 fatcat:j3odnadij5bgvjbynu2nobrbve

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-modality Cardiac Segmentation

Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng Ann Heng
2019 IEEE Access  
In this paper, we propose a plug-and-play adversarial domain adaptation network (PnP-AdaNet) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT.  ...  We validate our domain adaptation method on cardiac segmentation in unpaired MRI and CT, with four different anatomical structures.  ...  [49] found that CycleGAN based medical image translations models trained on imbalanced datasets would hide dangerous brain tumors in synthetic images.  ... 
doi:10.1109/access.2019.2929258 fatcat:u4nuxyrzvzerfbrocgg44t6k5m

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [article]

Tao Zhou, Huazhu Fu, Yu Zhang, Changqing Zhang, Xiankai Lu, Jianbing Shen, Ling Shao
2020 arXiv   pre-print
., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively exploit the complementary information across multiple modalities and also preserve the modality-specific  ...  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients.  ...  Data Description and Processing The BraTS 2018 was organized using multi-institutional pre-operative MRI scans for the segmentation of intrinsically heterogeneous brain tumor sub-regions [18, 7] .  ... 
arXiv:2006.10135v2 fatcat:blzhuroxyzgo7crgim4nmcgzse
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