Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Segmentation of Cone Beam CT in Stereotactic Radiosurgery
KTH, School of Technology and Health (STH).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Segmentering av Cone Beam CT I stereotaktisk radiokirurgi (Swedish)
Abstract [en]

C-arm Cone Beam CT (CBCT) systems – due to compact size, flexible geometry and low radiation exposure – inaugurated the era of on-board 3D image guidance in therapeutic and surgical procedures. Leksell Gamma Knife Icon by Elekta introduced an integrated CBCT system to determine patient position prior to surgical session, thus advancing to a paradigm shift in facilitating frameless stereotactic radiosurgeries. While CBCT offers a quick imaging facility with high spatial accuracy, the quantitative values tend to be distorted due to various physics based artifacts such as scatter, beam hardening and cone beam effect. Several 3D reconstruction algorithms targeting these artifacts involve an accurate and fast segmentation of craniofacial CBCT images into air, tissue and bone.

The objective of the thesis is to investigate the performance of deep learning based convolutional neural networks (CNN) in relation to conventional image processing and machine learning algorithms in segmenting CBCT images. CBCT data for training and testing procedures was provided by Elekta. A framework of segmentation algorithms including multilevel automatic thresholding, fuzzy clustering, multilayer perceptron and CNN is developed and tested against pre-defined evaluation metrics carrying pixel-wise prediction accuracy, statistical tests and execution times among others. CNN has proven its ability to outperform other segmentation algorithms throughout the evaluation metrics except for execution times. Mean segmentation error for CNN is found to be 0.4% with a standard deviation of 0.07%, followed by fuzzy clustering with mean segmentation error of 0.8% and a standard deviation of 0.12%. CNN based segmentation takes 500s compared to multilevel thresholding which requires ~1s on similar sized CBCT image.

The present work demonstrates the ability of CNN in handling artifacts and noise in CBCT images and maintaining a high semantic segmentation performance. However, further efforts targeting CNN execution speed are required to utilize the segmentation framework within real-time 3D reconstruction algorithms.

Abstract [sv]

C-arm Cone Beam CT (CBCT) system har tack vare sitt kompakta format, flexibla geometri och låga strålningsdos startat en era av inbyggda 3D bildtagningssystem för styrning av terapeutiska och kirurgiska ingripanden. Elektas Leksell Gamma Knife Icon introducerade ett integrerat CBCT-system för att bestämma patientens position för operationer och på så sätt gå in i en paradigm av ramlös stereotaktisk strålkirurgi. Även om CBCT erbjuder snabb bildtagning med hög spatiel noggrannhet så tenderar de kvantitativa värdena att störas av olika artefakter som spridning, beam hardening och cone beam effekten. Ett flertal 3D rekonstruktionsalgorithmer som försöker reducera dessa artefakter kräver en noggrann och snabb segmentering av kraniofaciala CBCT-bilder i luft, mjukvävnad och ben.

Målet med den här avhandlingen är att undersöka hur djupa neurala nätverk baserade på faltning (convolutional neural networks, CNN) presterar i jämförelse med konventionella bildbehandlings- och maskininlärningalgorithmer för segmentering av CBCT-bilder. CBCT-data för träning och testning tillhandahölls av Elekta. Ett ramverk för segmenteringsalgorithmer inklusive flernivåströskling (multilevel automatic thresholding), suddig klustring (fuzzy clustering), flerlagersperceptroner (multilayer perceptron) och CNN utvecklas och testas mot fördefinerade utvärderingskriterier som pixelvis noggrannhet, statistiska tester och körtid. CNN presterade bäst i alla metriker förutom körtid. Det genomsnittliga segmenteringsfelet för CNN var 0.4% med en standardavvikelse på 0.07%, följt av suddig klustring med ett medelfel på 0.8% och en standardavvikelse på 0.12%. CNN kräver 500 sekunder jämfört med ungefär 1 sekund för den snabbaste algorithmen, flernivåströskling på lika stora CBCT-volymer.

Arbetet visar CNNs förmåga att handera artefakter och brus i CBCT-bilder och bibehålla en högkvalitativ semantisk segmentering. Vidare arbete behövs dock för att förbättra presetandan hos algorithmen för att metoden ska vara applicerbar i realtidsrekonstruktionsalgorithmer.

Place, publisher, year, edition, pages
2016. , 56 p.
Series
TRITA-STH, 2016:104
Keyword [en]
Cone Beam CT, Convolutional Neural Networks, Image Segmentation, Leksell Gamma Knife Icon, Deep Learning.
Keyword [sv]
Cone Beam CT, neurala nätverk baserade på faltning, bildsegmentering, Leksell Gamma Knife Icon, Djup inlärning.
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-193107OAI: oai:DiVA.org:kth-193107DiVA: diva2:975179
External cooperation
Elekta Instrument AB
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Presentation
2016-09-02, T-63, Hälsovägen 11C, Huddinge, Sweden, 12:00 (English)
Supervisors
Examiners
Available from: 2016-09-29 Created: 2016-09-28 Last updated: 2016-09-29Bibliographically approved

Open Access in DiVA

Ashfaq_Master_Thesis(1858 kB)333 downloads
File information
File name FULLTEXT01.pdfFile size 1858 kBChecksum SHA-512
257aa1181393e425a8b27000326f136121060acc1e6b711c422658d1f7723d51ee3c6b3443bd48820ccd5c56ca324c3db8d92025f9422edb592d4be8069258f4
Type fulltextMimetype application/pdf

By organisation
School of Technology and Health (STH)
Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 333 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1052 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf