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Automated Liver Segmentation from MR-Images Using Neural Networks
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). (MR-Physics)
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatiserad leversegmentering av MR-bilder med neurala nätverk (Swedish)
Abstract [en]

Liver segmentation is a cumbersome task when done manually, often consuming quality time of radiologists. Use of automation in such clinical task is fundamental and the subject of most modern research. Various computer aided methods have been incorporated for this task, but it has not given optimal results due to the various challenges faced as low-contrast in the images, abnormalities in the tissues, etc. As of present, there has been significant progress in machine learning and artificial intelligence (AI) in the field of medical image processing. Though challenges exist, like image sensitivity due to different scanners used to acquire images, difference in imaging methods used, just to name a few. The following research embodies a convolutional neural network (CNN) was incorporated for this process, specifically a U-net algorithm. Predicted masks are generated on the corresponding test data and the Dice similarity coefficient (DSC) is used as a statistical validation metric for performance evaluation. Three datasets, from different scanners (two1.5 T scanners and one 3.0 T scanner), have been evaluated. The U-net performs well on the given three different datasets, even though there was limited data for training, reaching upto DSC of 0.93 for one of the datasets.

Place, publisher, year, edition, pages
2019. , p. 72
Keywords [en]
liver segmentation, neural network, mri, mr images, segmentation, unet, deep learning, image processing, abdominal mri
Keywords [sv]
leversegmentering, mr bilder, neurala nätverk, segmentering
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-162599ISRN: LIU-IMH/RV-A-19/002-SEOAI: oai:DiVA.org:liu-162599DiVA, id: diva2:1377018
Subject / course
Master's Programme in Biomedical Engineering
Presentation
2019-11-06, Wrannesalen, CMIV, Universititetssjukhuset, Linköping, 09:00 (English)
Supervisors
Examiners
Available from: 2020-01-23 Created: 2019-12-10 Last updated: 2020-01-23Bibliographically approved

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17a436c973f3d748b6c48621a479d511f01f40b3a7a9d90942e2ff5b700f3588ddb901045ba9ca1efbc948f128a6b8746b69f11b5e71ecd7f1faff2bab516e8f
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Division of Radiological SciencesCenter for Medical Image Science and Visualization (CMIV)
Radiology, Nuclear Medicine and Medical ImagingMedical Image Processing

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