Literature DB >> 23743802

A survey of MRI-based medical image analysis for brain tumor studies.

Stefan Bauer1, Roland Wiest, Lutz-P Nolte, Mauricio Reyes.   

Abstract

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

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Year:  2013        PMID: 23743802     DOI: 10.1088/0031-9155/58/13/R97

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  92 in total

Review 1.  Segmentation of human brain using structural MRI.

Authors:  Gunther Helms
Journal:  MAGMA       Date:  2016-01-06       Impact factor: 2.310

2.  A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

Authors:  Bjoern H Menze; Koen Van Leemput; Danial Lashkari; Tammy Riklin-Raviv; Ezequiel Geremia; Esther Alberts; Philipp Gruber; Susanne Wegener; Marc-Andre Weber; Gabor Szekely; Nicholas Ayache; Polina Golland
Journal:  IEEE Trans Med Imaging       Date:  2015-11-20       Impact factor: 10.048

3.  Within-brain classification for brain tumor segmentation.

Authors:  Mohammad Havaei; Hugo Larochelle; Philippe Poulin; Pierre-Marc Jodoin
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-11-03       Impact factor: 2.924

4.  Deep learning-based image restoration algorithm for coronary CT angiography.

Authors:  Fuminari Tatsugami; Toru Higaki; Yuko Nakamura; Zhou Yu; Jian Zhou; Yujie Lu; Chikako Fujioka; Toshiro Kitagawa; Yasuki Kihara; Makoto Iida; Kazuo Awai
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

5.  Deep learning with mixed supervision for brain tumor segmentation.

Authors:  Pawel Mlynarski; Hervé Delingette; Antonio Criminisi; Nicholas Ayache
Journal:  J Med Imaging (Bellingham)       Date:  2019-08-10

Review 6.  Machine Learning for Medical Imaging.

Authors:  Bradley J Erickson; Panagiotis Korfiatis; Zeynettin Akkus; Timothy L Kline
Journal:  Radiographics       Date:  2017-02-17       Impact factor: 5.333

7.  Brain tumor segmentation using holistically nested neural networks in MRI images.

Authors:  Ying Zhuge; Andra V Krauze; Holly Ning; Jason Y Cheng; Barbara C Arora; Kevin Camphausen; Robert W Miller
Journal:  Med Phys       Date:  2017-08-20       Impact factor: 4.071

8.  Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

Authors:  Shikha Chaganti; Louise A Mawn; Hakmook Kang; Josephine Egan; Susan M Resnick; Lori L Beason-Held; Bennett A Landman; Thomas A Lasko
Journal:  IEEE J Biomed Health Inform       Date:  2018-12-28       Impact factor: 5.772

9.  Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials.

Authors:  James S Cordova; Eduard Schreibmann; Costas G Hadjipanayis; Ying Guo; Hui-Kuo G Shu; Hyunsuk Shim; Chad A Holder
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

Review 10.  Radiological images and machine learning: Trends, perspectives, and prospects.

Authors:  Zhenwei Zhang; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2019-02-27       Impact factor: 4.589

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