Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU115017" target="_blank" >RIV/00216305:26220/15:PU115017 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-42016-5_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-42016-5_6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-42016-5_6" target="_blank" >10.1007/978-3-319-42016-5_6</a>

Result language
angličtina
Original language name
Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation
Original language description
Most medical images feature a high similarity in the intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with - and even exploiting - this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the "local structure prediction" of local label patches. In the present study we test this local structure prediction approach for 3D segmentation tasks, systematically evaluating different parameters that are relevant for the dense annotation of anatomical structures. We choose convolutional neural network as learning algorithm, as it is known to be suited for dealing with correlation between features. We evaluate our approach on the public BRATS2014 data set with three multimodal segmentation tasks, being able to obtain state-of-the-art results for this brain tumor segmentation data set consisting of 254 multimodal volumes with computing time of only 13 seconds per volume.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LD14091" target="_blank" >LD14091: Primary and soft biometric and non-biometric traits in the de-identification process of audio-visual content in multimedia data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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