Computational optimisation of optical projection tomography for 3D image analysis

Leiden Repository

Computational optimisation of optical projection tomography for 3D image analysis

Type: Doctoral Thesis
Title: Computational optimisation of optical projection tomography for 3D image analysis
Author: Tang, X.
Issue Date: 2020-06-10
Keywords: Optical projection tomography
3D image reconstruction
Centre of rotation correction
Point spread function
Image segmentation
Deep learning
Convolutional neural network
Tumor quantification
Automated annotation
Abstract: Optical projection tomography (OPT) is a tomographic 3D imaging technique used for specimens in the millimetre scale. 3D images are computed from a tomogram and therefore OPT is considered as computational imaging. In order to provide imaging and image analysis solutions for large scale biomedical research, optimisation of the OPT reconstruction is required. The aim of the optimisation presented in this thesis includes: (1) accelerate the reconstruction process; (2) reduce the reconstruction artefacts; (3) improve the image quality of 3D image; (4) Find optimal parameters for the iterative reconstruction.Starting from the optimisations that we have elaborated and implemented in the OPT imaging workflow, we have worked on case studies in zebrafish imaging. In this thesis we present one such particular case study (5) as it falls nicely in the order of magnitude for specimens in OPT imaging. The case study is on quantification of tumours in zebrafish and it is explored with image segmentation and object detection using artificial intelligence (AI) techniques.
Promotor: Supervisor: Verbeek F.J.
Faculty: Faculty of Science
University: Leiden University
Uri: urn:isbn:9789463326384
Handle: http://hdl.handle.net/1887/106088
 

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application/pdf Chapter 1 1.154Mb View/Open
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application/pdf Chapter 7 300.6Kb View/Open
application/pdf Bibliography 364.5Kb View/Open
application/pdf Summary in English 186.5Kb View/Open
application/pdf Summary in Dutch 188.0Kb View/Open
application/pdf Curriculum Vitae_Acknowledgements 369.0Kb View/Open
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