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Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis [article]

Francesco La Rosa, Mário João Fartaria, Tobias Kober, Jonas Richiardi, Cristina Granziera, Jean-Philippe Thiran, Meritxell Bach Cuadra
2018 arXiv   pre-print
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.  ...  Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%).  ...  Acknowledgements The work is supported by the Centre d Imagerie BioMédicale (CIBM) of the University of Lausanne (UNIL), the Swiss Federal Institute of Technology Lausanne (EPFL), the University of Geneva  ... 
arXiv:1809.03185v1 fatcat:ef6blrpuvjgvte5ijnsa7nmhdu

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

Gulay Macin, Burak Tasci, Irem Tasci, Oliver Faust, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
2022 Applied Sciences  
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI).  ...  It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.  ...  Interpretation of MRI images requires experts to manually scrutinize multiple contiguous image sections for the presence of white matter lesions, with care being taken to distinguish MS plaques from lesions  ... 
doi:10.3390/app12104920 fatcat:4rghnftxvfgbrcrnz43kdi3rfq

Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

Shahab Aslani, Michael Dayan, Loredana Storelli, Massimo Filippi, Vittorio Murino, Maria A. Rocca, Diego Sona
2019 NeuroImage  
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images.  ...  Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data.  ...  Introduction Multiple sclerosis (MS) is a chronic, autoimmune and demyelinating disease of the central nervous system causing lesions in the brain tissues, notably in white matter (WM) (Steinman, 1996  ... 
doi:10.1016/j.neuroimage.2019.03.068 pmid:30953833 fatcat:f3gl3sgqbfevxp4jit6muvr5yq

Deep Learning Based Brain Tumor Segmentation: A Survey [article]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions.  ...  Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques  ...  The goal of white matter lesion segmentation is to segment the white matter region from the normal tissue.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Deep Learning in Medical Image Analysis

Dinggang Shen, Guorong Wu, Heung-Il Suk
2017 Annual Review of Biomedical Engineering  
In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease  ...  On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images.  ...  (45) applied deep learning for multiple sclerosis lesion segmentation on MR images.  ... 
doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734 pmcid:PMC5479722 fatcat:amn6qgpt6fedzp3zejgi4aw66u

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 Journal of Magnetic Resonance Imaging  
Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson.  ...  In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms.  ...  Acknowledgments: The authors would like to thank Gemini Janas for reviewing and editing this article.  ... 
doi:10.1002/jmri.26534 pmid:30575178 pmcid:PMC6483404 fatcat:7jg5sr7z6bbehd6xabsjw6bcde

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Appendix A: Literature selection Pubmed was searched for papers containing "convolutional" OR "deep learning" in any field.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Deep learning in radiology: an overview of the concepts and a survey of the state of the art [article]

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 arXiv   pre-print
In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms.  ...  Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.  ...  The authors would like to thank Gemini Janas for reviewing and editing this article.  ... 
arXiv:1802.08717v1 fatcat:7qirj6hb2bdafnplc6au4wysqi

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Mariana Bento, Irene Fantini, Justin Park, Leticia Rittner, Richard Frayne
2022 Frontiers in Neuroinformatics  
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic  ...  However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures.  ...  We also gratefully acknowledge the support, including many helpful discussions, of the staff, fellows and students in the Calgary Image Analysis Processing Centre (CIPAC, https://cumming.ucalgary. ca/centre  ... 
doi:10.3389/fninf.2021.805669 pmid:35126080 pmcid:PMC8811356 fatcat:57znd2kfhfgkflil62jnnpkqm4

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Gokhan Guney, Busra Ozgode Yigin, Necdet Guven, Yasemin Hosgoren Alici, Burcin Colak, Gamze Erzin, Gorkem Saygili
2021 Clinical Psychopharmacology and Neuroscience  
Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data.  ...  In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture  ...  The results of the evaluation concluded that demyelination at the lesion sites and the myelin content in normal-appearing white matter could be predicted with high accuracy.  ... 
doi:10.9758/cpn.2021.19.2.206 pmid:33888650 pmcid:PMC8077051 fatcat:wiycvc4lffhdhh5ehi6jkn7nyi

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical  ...  What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years.  ...  Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article.  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis

Alina Lopatina, Stefan Ropele, Renat Sibgatulin, Jürgen R. Reichenbach, Daniel Güllmar
2020 Frontiers in Neuroscience  
The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging.  ...  The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data.  ...  DG designed and supervised the experiments, aided in interpreting results, and writing the manuscript. All authors discussed the results and commented on the manuscript.  ... 
doi:10.3389/fnins.2020.609468 pmid:33390890 pmcid:PMC7775402 fatcat:fptagzsvkrduznfj3aekwnszhu

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.  ...  This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community.  ...  In their work, Nair et al. [120] used a 3D CNN approach for the segmentation and detection of Multiple Sclerosis (MS) lesions in MRI sequences.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
promising directions for the Medical Imaging Community to fully harness deep learning in the future.  ...  This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community.  ...  [130] used a 3D CNN approach for the segmentation and detection of Multiple Sclerosis (MS) lesions in MRI sequences. Roy et al.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

Mindboggling morphometry of human brains

Arno Klein, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, Brian Rossa, Martin Reuter, Elias Chaibub Neto, Anisha Keshavan, Dina Schneidman
2017 PLoS Computational Biology  
In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans.  ...  We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist.  ...  We also thank Gang Li, Denis Rivière, and Olivier Coulon for assistance with the fundus evaluation.  ... 
doi:10.1371/journal.pcbi.1005350 pmid:28231282 pmcid:PMC5322885 fatcat:lsqa2kigtzfivlkqzrnmkri37u
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